Friday, 20 January 2017

An Algorithm to determine the context


The purpose of this note is to propose an algorithm to automatically detect the context of a given text.

Context means "1. The part of a text or statement that surrounds a particular word or passage and determines its meaning.
2. The circumstances in which an event occurs; a setting." according to Free Dictionary.

I used 3 texts as example for the experiment.  These are fairytales named "The Princess and the Pea"
and "The Little Match-Seller" of Hans Christian Andersen, "The Frog Prince" of Grimm Brothers.

My algorithm is as follows:

1- Determine each sentence that each word is used in:

Little===>0,1,3,5,5,8,12,14,18,20,27,32,35,37,39,40,40
Match-Seller===>0
It===>0,2,4,4,10,13,16,16,17,17,18,18,19,20,25,25,30,31,34
was===>0,0,10,10,13,19,20,26,27,30,35,35,38,39,41
terribly===>0

2-Determine which two words are used in the same sentences of the text:
across==>3,27     (PS. ARS =across is used in the 3. and 27. sentences)
ctx(0)= 3           (PS. ARS = ctx stands for shared context)
host=60 ctxList=(3,27) hostCTX=3 across .............street (3)...............dest=61
host=60 ctxList=(3,27) hostCTX=3 across .............avoid (3)...............dest=62
host=60 ctxList=(3,27) hostCTX=3 across .............two (3,11)...............dest=63
host=60 ctxList=(3,27) hostCTX=3 across .............carriages (3)...............dest=64
host=60 ctxList=(3,27) hostCTX=3 across .............rolling (3)...............dest=65
host=60 ctxList=(3,27) hostCTX=3 across .............along (3,8)...............dest=66
host=60 ctxList=(3,27) hostCTX=3 across .............terrible (3)...............dest=68
host=60 ctxList=(3,27) hostCTX=3 across .............rate (3)...............dest=69
ctx(1)= 27
host=60 ctxList=(3,27) hostCTX=27 across .............goose (10,26,27,37)...............dest=134
host=60 ctxList=(3,27) hostCTX=27 across .............down (11,27,31)...............dest=147
host=60 ctxList=(3,27) hostCTX=27 across .............wonderful (19,27)...............dest=211
host=60 ctxList=(3,27) hostCTX=27 across .............its (25,27)...............dest=234
host=60 ctxList=(3,27) hostCTX=27 across .............still (27,41)...............dest=255
host=60 ctxList=(3,27) hostCTX=27 across .............more (27,30)...............dest=256
host=60 ctxList=(3,27) hostCTX=27 across .............jumped (27)...............dest=257
host=60 ctxList=(3,27) hostCTX=27 across .............dish (27)...............dest=258
host=60 ctxList=(3,27) hostCTX=27 across .............waddled (27)...............dest=259
host=60 ctxList=(3,27) hostCTX=27 across .............floor (27)...............dest=260
host=60 ctxList=(3,27) hostCTX=27 across .............knife (27)...............dest=261
host=60 ctxList=(3,27) hostCTX=27 across .............fork (27)...............dest=262
host=60 ctxList=(3,27) hostCTX=27 across .............breast (27)...............dest=263

3- Determine the sentences that are common for any two words:
------------------------>contextIDLIST
===============>>>1=Little 6=cold sentence=The Little Match-Seller

It was terribly cold and nearly dark on the last evening of the old year, and the snow was falling fast.

===============>>>1=Little 11=last sentence=The Little Match-Seller

It was terribly cold and nearly dark on the last evening of the old year, and the snow was falling fast.
===============>>>1=Little 12=evening sentence=The Little Match-Seller

...
===============>>>1=Little 6=cold sentence= In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.
===============>>>1=Little 22=poor sentence= In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.
===============>>>1=Little 23=girl sentence= In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.
===============>>>1=Little 27=naked sentence= In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.
===============>>>1=Little 28=feet sentence= In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.
...

4-Count the freq of two words(word couples) that are used in/per the text.

------------------------>countFreqContextIdentifications

freq=6===============>1=Little 6=cold sentence=0
The Little Match-Seller

It was terribly cold and nearly dark on the last evening of the old year, and the snow was falling fast.

freq=6===============>1=Little 6=cold sentence=1
 In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.

freq=3===============>1=Little 22=poor sentence=1
 In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.

freq=5===============>1=Little 23=girl sentence=1
 In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.

The output displays the related sentences together with the frequency of the word couple.

The results for the three chosen texts, fairytales follows:

The Little Match-Seller
***********************
freq=6===============>1=Little 6=cold sentence=0
Little===>0,1,3,5,5,8,12,14,18,20,27,32,35,37,39,40,40
cold===>0,1,5,8,12,13,14,28,39
freq=5===============>1=Little 23=girl sentence=1
girl===>1,5,20,27,35,39
freq=3===============>1=Little 22=poor
freq=3===============>1=Little 28=feet
freq=3===============>1=Little 70=One
freq=3===============>183=match 192=wall

The text of the fairy tales follows:

0===>The Little Match-Seller

It was terribly cold and nearly dark on the last evening of the old year, and the snow was falling fast.<===
1===> In the cold and the darkness, a poor little girl, with bare head and naked feet, roamed through the streets.<===
2===> It is true she had on a pair of slippers when she left home, but they were not of much use.<===
3===> They were very large, so large, indeed, that they had belonged to her mother, and the poor little creature had lost them in running across the street to avoid two carriages that were rolling along at a terrible rate.<===
4===> One of the slippers she could not find, and a boy seized upon the other and ran away with it, saying that he could use it as a cradle, when he had children of his own.<===
5===> So the little girl went on with her little naked feet, which were quite red and blue with the cold.<===
6===>

     In an old apron she carried a number of matches, and had a bundle of them in her hands.<===
7===> No one had bought anything of her the whole day, nor had any one given here even a penny.<===
8===> Shivering with cold and hunger, she crept along; poor little child, she looked the picture of misery.<===
9===> The snowflakes fell on her long, fair hair, which hung in curls on her shoulders, but she regarded them not.<===
10===>

     Lights were shining from every window, and there was a savory smell of roast goose, for it was New-year's eve - yes, she remembered that.<===
11===> In a corner, between two houses, one of which projected beyond the other, she sank down and huddled herself together.<===
12===> She had drawn her little feet under her, but she could not keep off the cold; and she dared not go home, for she had sold no matches, and could not take home even a penny of money.<===
13===> Her father would certainly beat her; besides, it was almost as cold at home as here, for they had only the roof to cover them, through which the wind howled, although the largest holes had been stopped up with straw and rags.<===
14===>

     Her little hands were almost frozen with the cold.<===
15===> Ah!<===
16===> perhaps a burning match might be some good, if she could draw it from the bundle and strike it against the wall, just to warm her fingers.<===
17===>

     She drew one out - "scratch!" how it sputtered as it burnt!<===
18===> It gave a warm, bright light, like a little candle, as she held her hand over it.<===
19===> It was really a wonderful light.<===
20===> It seemed to the little girl that she was sitting by a large iron stove, with polished brass feet and a brass ornament.<===
21===> How the fire burned!<===
22===> and seemed so beautifully warm that the child stretched out her feet as if to warm them, when, lo!<===
23===> the flame of the match went out, the stove vanished, and she had only the remains of the half-burnt match in her hand.<===
24===>

     She rubbed another match on the wall.<===
25===> It burst into a flame, and where its light fell upon the wall it became as transparent as a veil, and she could see into the room.<===
26===> The table was covered with a snowy white table-cloth, on which stood a splendid dinner service, and a steaming roast goose, stuffed with apples and dried plums.<===
27===> And what was still more wonderful, the goose jumped down from the dish and waddled across the floor, with a knife and fork in its breast, to the little girl.<===
28===> Then the match went out, and there remained nothing but the thick, damp, cold wall before her.<===
29===>

     She lighted another match, and then she found herself sitting under a beautiful Christmas-tree.<===
30===> It was larger and more beautifully decorated than the one which she had seen through the glass door at the rich merchant's.<===
31===> Thousands of tapers were burning upon the green branches, and colored pictures, like those she had seen in the show-windows, looked down upon it all.<===
32===> The little one stretched out her hand towards them, and the match went out.<===
33===>

     The Christmas lights rose higher and higher, till they looked to her like the stars in the sky.<===
34===> Then she saw a star fall, leaving behind it a bright streak of fire.<===
35===> "Some one is dying," thought the little girl, for her old grandmother, the only one who had ever loved her, and who was now dead, had told her that when a star falls, a soul was going up to God.<===
36===>

     She again rubbed a match on the wall, and the light shone round her; in the brightness stood her old grandmother, clear and shining, yet mild and loving in her appearance.<===
37===>

     "Grandmother," cried the little one, "O take me with you; I know you will go away when the match burns out; you will vanish like the warm stove, the roast goose, and the large, glorious Christmas-tree."

     And she made haste to light the whole bundle of matches, for she wished to keep her grandmother there.<===
38===> And the matches glowed with a light that was brighter than the noon-day, and her grandmother had never appeared so large or so beautiful.<===
39===> She took the little girl in her arms, and they both flew upwards in brightness and joy far above the earth, where there was neither cold nor hunger nor pain, for they were with God.<===
40===>

     In the dawn of morning there lay the poor little one, with pale cheeks and smiling mouth, leaning against the wall; she had been frozen to death on the last evening of the year; and the New-year's sun rose and shone upon a little corpse!<===
41===> The child still sat, in the stiffness of death, holding the matches in her hand, one bundle of which was burnt.<===
42===>

     "She tried to warm herself," said some.<===
43===>

     No one imagined what beautiful things she had seen, nor into what glory she had entered with her grandmother, on New-year's day.<===

The results for the other test texts:
The Princess and the Pea
************************
freq=5===============>4=Princess 23=real l sentence=0
Once upon a time there was a prince who wanted to marry a princess; but she would have to be a real princess.
freq=5===============>4=Princess 23=real sentence=4
 So he came home again and was sad, for he would have liked very much to have a real princess.
freq=5===============>4=Princess 23=real sentence=11
 And yet she said that she was a real princess.
freq=5===============>4=Princess 23=real sentence=17
 "I have scarcely closed my eyes all night. Heaven only knows what was in the bed, but I was lying on something hard, so that I am black and blue all over my body. It's horrible!"

     Now they knew that she was a real princess because she had felt the pea right through the twenty mattresses and the twenty eider-down beds.
freq=5===============>4=Princess 23=real sentence=18


     Nobody but a real princess could be as sensitive as that.
freq=5===============>4=Princess 23=real sentence=19


     So the prince took her for his wife, for now he knew that he had a real princess; and the pea was put in the museum, where it may still be seen, if no one has stolen it.

The Frog Prince
***************
freq=11===============>3=Frog 10=princess sentence=5
     Whilst she was speaking, a frog put its head out of the water, and said, "Princess, why do you weep so bitterly?"
freq=11===============>3=Frog 10=princess sentence=7
     "What nonsense," thought the princess, "this silly frog is talking! He can never even get out of the spring to visit me, though he may be able to get my ball for me, and therefore I will tell him he shall have what he asks".
freq=11===============>3=Frog 10=princess sentence=10
     As soon as the young princess saw her ball, she ran to pick it up; and she was so overjoyed to have it in her hand again, that she never thought of the frog, but ran home with it as fast as she could.
freq=11===============>3=Frog 10=princess sentence=11
     The frog called after her, "Stay, princess, and take me with you as you said,"

Sunday, 1 January 2017

LANGANA translation outputs after optimization

Good news from the LANGANA English to Turkish translator!!!

Please find below 100 test sentences and a seperate single test sentence that I have used to test the performancce of LANGANA.

You may test these with the new GOOGLE translator program.  You will notice that GOOGLE translates much faster but not very accurate.

I have written most of LANGANA again to improve the speed from 3 minutes 40 seconds to 30 seconds for 100 test sentences.  The speed for a single sentence got reduced from 11 seconds to 5.7 seconds.

LANGANA is and can be made inherently more accurate than GOOGLE's neural networks approach.
LANGANA produces an accurate PARSE outpur which you can find below.  This output may be used for automatic test processing applications even text understanding and automatic question
answering.

At this point I decided to put LANGANA as a free service on AMAZON free-tier aiming excellent but a little bit slow  translations.

We have proceeded to make a TUBITAK application to get ample resources and to make extraordinary application changes and create a new translator application with the support we hope to get.  Unfortunately this process is commencing slowly.

All the best for the new year.

Ali R+


C:\Users\ars\ARSlanganae\interactiveTRANS>REM interactive translate
STEP0 begins. -  inits DB ResetDB.java + erase transOutFILE.txt - Main-Class: fileAccess.ResetDB
        1 file(s) copied.
STEP1 begins. - reads txt input file - inserts to DB
STEP2 begins. - reads DB - inserts to DB   (nbprocesskandeldb.NbSetDetInfosAndUpdateINTERACTIVEDB)
STEP3 begins. - reads DB - outputs to txt file - nbAnalyseINTERACTIVE properties - nbprocesssentence.NbProcessSentenceINTERACTIVEDB
        1 file(s) copied.
STEP4 begins. txt input file - outputs to txt file  /uploadAnalyseINTERACTIVEDB
        1 file(s) copied.
STEP5 begins. - reads txt input file - outputs to DB /uploadAnalyseINTERACTIVEDBmanual
STEP6 begins. - reads DB - outputs to txt file - nbfindstruct.NbProcessSentenceINTERACTIVEDB JAR should be generated with this RUN command


BEFORE SPEED OPTIMIZATION  <===================
test with 100 complex sentences given below

command took 0:2:49.28 (169.28s total)
STEP0 took 0:0:0.90 (0.90s total)
STEP1 took 0:0:32.65 (32.65s total)
STEP2 took 0:1:14.49 (74.49s total)
STEP3 took 0:0:19.81 (19.81s total)
STEP4 took 0:0:0.83 (0.83s total)
STEP5 took 0:0:32.47 (32.47s total)
STEP6 took 0:0:8.13 (8.13s total)

test with 1 SIMPLE SENTENCE   "I am Ali."

command took 0:2:41.58 (161.58s total)
STEP0 took 0:0:0.83 (0.83s total)
STEP1 took 0:0:30.00 (30.00s total)
STEP2 took 0:1:12.90 (72.90s total)
STEP3 took 0:0:12.79 (12.79s total)
STEP4 took 0:0:0.82 (0.82s total)
STEP5 took 0:0:36.45 (36.45s total)
STEP6 took 0:0:7.79 (7.79s total)

AFTER SPEED OPTIMIZATION  <===================
test with 100 COMPLEX SENTENCES given below

command took 0:0:30.20 (30.20s total)
STEP0 took 0:0:0.85 (0.85s total)
STEP1 took 0:0:0.84 (0.84s total)
STEP2 took 0:0:0.21 (0.21s total)
STEP3 took 0:0:16.51 (16.51s total)
STEP4 took 0:0:1.02 (1.02s total)
STEP5 took 0:0:1.38 (1.38s total)
STEP6 took 0:0:9.39 (9.39s total)

test with 1 SIMPLE SENTENCE   "I am Ali."

command took 0:0:5.71 (5.71s total)
STEP0 took 0:0:0.78 (0.78s total)
STEP1 took 0:0:0.77 (0.77s total)
STEP2 took 0:0:0.17 (0.17s total)
STEP3 took 0:0:1.10 (1.10s total)
STEP4 took 0:0:0.80 (0.80s total)
STEP5 took 0:0:0.79 (0.79s total)
STEP6 took 0:0:1.30 (1.30s total)

100 TEST SENTENCES
*******************
I visited him who I was going.
I visited him who I had went.
I visited him who I would go.
I visited him who I could go.
I visited him who I should go.
I visited him who I shall go.
I visited him who I went.
I visited him who I go.
I visited him who I can go.
I visited him who I may go.
I visited him who I will go.
I visited him who I am going.
I visited him who I have gone.

I visited them who were going to school.
I visited them who had went to school.
I visited them who would go to school.
I visited them who could go to school.
I visited them who should go to school.
I visited them who shall go to school.
I visited them who went to school.
I visited them who go to school.
I visited them who can go to school.
I visited them who may go to school.
I visited them who will go to school.
I visited them who are going to school.
I visited them who have gone to school.
I visited him who I was helping.
I visited him who I had helped.
I visited him who I would help.
I visited him who I could help.
I visited him who I should help.
I visited him who I shall help.
I visited him who I helped.
I visited him who I help.
I visited him who I can help.
I visited him who I may help.
I visited him who I will help.
I visited him who I am helping.
I visited him who I have helped.

I visited them who were helping me.
I visited them who had helped me.
I visited them who would help me.
I visited them who could help me.
I visited them who should help me.
I visited them who shall help me.
I visited them who helped me.
I visited them who help me.
I visited them who can help me.
I visited them who may help me.
I visited them who will help me.
I visited them who are helping me.
I visited them who have helped me.
I visited him who I was kissing.
I visited him who I had kissed.
I visited him who I would kiss.
I visited him who I could kiss.
I visited him who I should kiss.
I visited him who I shall kiss.
I visited him who I kissed.
I visited him who I kiss.
I visited him who I can kiss.
I visited him who I may kiss.
I visited him who I will kiss.
I visited him who I am kissing.
I visited him who I have kissed.

I visited them who were kissing me.
I visited them who had kissed me.
I visited them who would kiss me.
I visited them who could kiss me.
I visited them who should kiss me.
I visited them who shall kiss me.
I visited them who kissed me.
I visited them who kiss me.
I visited them who can kiss me.
I visited them who may kiss me.
I visited them who will kiss me.
I visited them who are kissing me.
I visited them who have kissed me.
I visited him who I was pushing.
I visited him who I had pushed.
I visited him who I would push.
I visited him who I could push.
I visited him who I should push.
I visited him who I shall push.
I visited him who I pushed.
I visited him who I push.
I visited him who I can push.
I visited him who I may push.
I visited him who I will push.
I visited him who I am pushing.
I visited him who I have pushed.
End.

I am Ali.
End.

OUTPUTS
***************************
 I am Ali.
Ben aliyim.
 I visited him who I was going.
Ben gidiyor olduğum onu ziyaret ettim.
. I visited him who I had went.
Ben gitmiş olduğum onu ziyaret ettim.
. I visited him who I would go.
Ben gidecek olduğum onu ziyaret ettim.
. I visited him who I could go.
Ben gidebildiğim onu ziyaret ettim.
. I visited him who I should go.
Ben gitmek zorunda olduğum onu ziyaret ettim.
. I visited him who I shall go.
Ben gitmem gereken onu ziyaret ettim.
. I visited him who I went.
Ben gittiğim onu ziyaret ettim.
. I visited him who I go.
Ben gittiğim onu ziyaret ettim.
. I visited him who I can go.
Ben gidebildiğim onu ziyaret ettim.
. I visited him who I may go.
Ben gidebildiğim onu ziyaret ettim.
. I visited him who I will go.
Ben gideceğim onu ziyaret ettim.
. I visited him who I am going.
Ben gitmekte olduğum onu ziyaret ettim.
. I visited him who I have gone.
Ben gitmiş olduğum onu ziyaret ettim.
. I visited them who were going to school.
Ben okula gidiyor olan onları ziyaret ettim.
. I visited them who had went to school.
Ben okula gitmiş olan onları ziyaret ettim.
. I visited them who would go to school.
Ben okula gidecek olan onları ziyaret ettim.
. I visited them who could go to school.
Ben okula gidebilen onları ziyaret ettim.
. I visited them who should go to school.
Ben okula gitmek zorunda olan onları ziyaret ettim.
. I visited them who shall go to school.
Ben okula gitmesi gereken onları ziyaret ettim.
. I visited them who went to school.
Ben okula giden onları ziyaret ettim.
. I visited them who go to school.
Ben okula giden onları ziyaret ettim.
. I visited them who can go to school.
Ben okula gidebilen onları ziyaret ettim.
. I visited them who may go to school.
Ben okula gidebilen onları ziyaret ettim.
. I visited them who will go to school.
Ben okula gidecek onları ziyaret ettim.
. I visited them who are going to school.
Ben okula gitmekte olduğu onları ziyaret ettim.
. I visited them who have gone to school.
Ben okula gitmiş olan onları ziyaret ettim.
. I visited him who I was helping.
Ben yardım ediyor olduğum onu ziyaret ettim.
. I visited him who I had helped.
Ben yardım etmiş olduğum onu ziyaret ettim.
. I visited him who I would help.
Ben yardım edecek olduğum onu ziyaret ettim.
. I visited him who I could help.
Ben yardım edebildiğim onu ziyaret ettim.
. I visited him who I should help.
Ben yardım etmek zorunda olduğum onu ziyaret ettim.
. I visited him who I shall help.
Ben yardım etmem gereken onu ziyaret ettim.
. I visited him who I helped.
Ben yardım ettiğim onu ziyaret ettim.
. I visited him who I help.
Ben yardım ettiğim onu ziyaret ettim.
. I visited him who I can help.
Ben yardım edebildiğim onu ziyaret ettim.
. I visited him who I may help.
Ben yardım edebildiğim onu ziyaret ettim.
. I visited him who I will help.
Ben yardım edeceğim onu ziyaret ettim.
. I visited him who I am helping.
Ben yardım etmekte olduğum onu ziyaret ettim.
. I visited him who I have helped.
Ben yardım etmiş olduğum onu ziyaret ettim.
. I visited them who were helping me.
Ben bana yardım ediyor olan onları ziyaret ettim.
. I visited them who had helped me.
Ben bana yardım etmiş olan onları ziyaret ettim.
. I visited them who would help me.
Ben bana yardım edecek olan onları ziyaret ettim.
. I visited them who could help me.
Ben bana yardım edebilen onları ziyaret ettim.
. I visited them who should help me.
Ben bana yardım etmek zorunda olan onları ziyaret ettim.
. I visited them who shall help me.
Ben bana yardım etmesi gereken onları ziyaret ettim.
. I visited them who helped me.
Ben bana yardım eden onları ziyaret ettim.
. I visited them who help me.
Ben bana yardım eden onları ziyaret ettim.
. I visited them who can help me.
Ben bana yardım edebilen onları ziyaret ettim.
. I visited them who may help me.
Ben bana yardım edebilen onları ziyaret ettim.
. I visited them who will help me.
Ben bana yardım edecek onları ziyaret ettim.
. I visited them who are helping me.
Ben bana yardım etmekte olduğu onları ziyaret ettim.
. I visited them who have helped me.
Ben bana yardım etmiş olan onları ziyaret ettim.
. I visited him who I was kissing.
Ben öpüyor olduğum onu ziyaret ettim.
. I visited him who I had kissed.
Ben öpmüş olduğum onu ziyaret ettim.
. I visited him who I would kiss.
Ben öpecek olduğum onu ziyaret ettim.
. I visited him who I could kiss.
Ben öpebildiğim onu ziyaret ettim.
. I visited him who I should kiss.
Ben öpmek zorunda olduğum onu ziyaret ettim.
. I visited him who I shall kiss.
Ben öpmem gereken onu ziyaret ettim.
. I visited him who I kissed.
Ben öptüğüm onu ziyaret ettim.
. I visited him who I kiss.
Ben öptüğüm onu ziyaret ettim.
. I visited him who I can kiss.
Ben öpebildiğim onu ziyaret ettim.
. I visited him who I may kiss.
Ben öpebildiğim onu ziyaret ettim.
. I visited him who I will kiss.
Ben öpeceğim onu ziyaret ettim.
. I visited him who I am kissing.
Ben öpmekte olduğum onu ziyaret ettim.
. I visited him who I have kissed.
Ben öpmüş olduğum onu ziyaret ettim.
. I visited them who were kissing me.
Ben beni öpüyor olan onları ziyaret ettim.
. I visited them who had kissed me.
Ben beni öpmüş olan onları ziyaret ettim.
. I visited them who would kiss me.
Ben beni öpecek olan onları ziyaret ettim.
. I visited them who could kiss me.
Ben beni öpebilen onları ziyaret ettim.
. I visited them who should kiss me.
Ben beni öpmek zorunda olan onları ziyaret ettim.
. I visited them who shall kiss me.
Ben beni öpmesi gereken onları ziyaret ettim.
. I visited them who kissed me.
Ben beni öpen onları ziyaret ettim.
. I visited them who kiss me.
Ben beni öpen onları ziyaret ettim.
. I visited them who can kiss me.
Ben beni öpebilen onları ziyaret ettim.
. I visited them who may kiss me.
Ben beni öpebilen onları ziyaret ettim.
. I visited them who will kiss me.
Ben beni öpecek onları ziyaret ettim.
. I visited them who are kissing me.
Ben beni öpmekte olduğu onları ziyaret ettim.
. I visited them who have kissed me.
Ben beni öpmüş olan onları ziyaret ettim.
. I visited him who I was pushing.
Ben itiyor olduğum onu ziyaret ettim.
. I visited him who I had pushed.
Ben itmiş olduğum onu ziyaret ettim.
. I visited him who I would push.
Ben itecek olduğum onu ziyaret ettim.
. I visited him who I could push.
Ben itebildiğim onu ziyaret ettim.
. I visited him who I should push.
Ben itmek zorunda olduğum onu ziyaret ettim.
. I visited him who I shall push.
Ben itmem gereken onu ziyaret ettim.
. I visited him who I pushed.
Ben ittiğim onu ziyaret ettim.
. I visited him who I push.
Ben ittiğim onu ziyaret ettim.
. I visited him who I can push.
Ben itebildiğim onu ziyaret ettim.
. I visited him who I may push.
Ben itebildiğim onu ziyaret ettim.
. I visited him who I will push.
Ben iteceğim onu ziyaret ettim.
. I visited him who I am pushing.
Ben itmekte olduğum onu ziyaret ettim.
. I visited him who I have pushed.
Ben itmiş olduğum onu ziyaret ettim.


PARSER OUTPUT
***************
SENTENCE 0 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 was going <------ p="" verb="">

SENTENCE 1 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 had went <------ p="" verb="">

SENTENCE 2 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 would go <------ p="" verb="">

SENTENCE 3 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 could go <------ p="" verb="">

SENTENCE 4 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 should go <------ p="" verb="">

SENTENCE 5 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 shall go <------ p="" verb="">

SENTENCE 6 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 6 went <------ p="" verb="">

SENTENCE 7 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 6 go <------ p="" verb="">

SENTENCE 8 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 can go <------ p="" verb="">

SENTENCE 9 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 may go <------ p="" verb="">

SENTENCE 10 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 will go <------ p="" verb="">

SENTENCE 11 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 am going <------ p="" verb="">

SENTENCE 12 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 have gone <------ p="" verb="">

SENTENCE 13 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 were going <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 14 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 had went <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 15 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 would go <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 16 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 could go <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 17 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 should go <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 18 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 shall go <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 19 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 went <------ p="" verb="">
5 7 to school <-----preposition p="" phrase="">

SENTENCE 20 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 go <------ p="" verb="">
5 7 to school <-----preposition p="" phrase="">

SENTENCE 21 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 can go <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 22 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 may go <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 23 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 will go <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 24 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 are going <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 25 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 8 who <-----------relative clause="" p="">
4 6 have gone <------ p="" verb="">
6 8 to school <-----preposition p="" phrase="">

SENTENCE 26 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 was helping <------ p="" verb="">

SENTENCE 27 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 had helped <------ p="" verb="">

SENTENCE 28 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 would help <------ p="" verb="">

SENTENCE 29 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 could help <------ p="" verb="">

SENTENCE 30 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 should help <------ p="" verb="">

SENTENCE 31 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 shall help <------ p="" verb="">

SENTENCE 32 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 6 helped <------ p="" verb="">

SENTENCE 33 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 6 help <------ p="" verb="">

SENTENCE 34 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 can help <------ p="" verb="">

SENTENCE 35 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 may help <------ p="" verb="">

SENTENCE 36 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 will help <------ p="" verb="">

SENTENCE 37 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 am helping <------ p="" verb="">

SENTENCE 38 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 have helped <------ p="" verb="">

SENTENCE 39 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 were helping <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 40 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 had helped <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 41 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 would help <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 42 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 could help <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 43 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 should help <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 44 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 shall help <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 45 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 helped <------ p="" verb="">
5 6 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 46 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 help <------ p="" verb="">
5 6 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 47 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 can help <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 48 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 may help <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 49 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 will help <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 50 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 are helping <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 51 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 have helped <------ p="" verb="">
6 7 me ||obliq. pron.<----- dative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 52 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 was kissing <------ p="" verb="">

SENTENCE 53 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 had kissed <------ p="" verb="">

SENTENCE 54 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 would kiss <------ p="" verb="">

SENTENCE 55 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 could kiss <------ p="" verb="">

SENTENCE 56 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 should kiss <------ p="" verb="">

SENTENCE 57 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 shall kiss <------ p="" verb="">

SENTENCE 58 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 6 kissed <------ p="" verb="">

SENTENCE 59 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 6 kiss <------ p="" verb="">

SENTENCE 60 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 can kiss <------ p="" verb="">

SENTENCE 61 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 may kiss <------ p="" verb="">

SENTENCE 62 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 will kiss <------ p="" verb="">

SENTENCE 63 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 am kissing <------ p="" verb="">

SENTENCE 64 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 have kissed <------ p="" verb="">

SENTENCE 65 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 were kissing <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 66 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 had kissed <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 67 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 would kiss <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 68 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 could kiss <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 69 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 should kiss <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 70 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 shall kiss <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 71 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 kissed <------ p="" verb="">
5 6 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 72 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 kiss <------ p="" verb="">
5 6 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 73 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 can kiss <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 74 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 may kiss <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 75 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 will kiss <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 76 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 are kissing <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 77 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 them ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 6 have kissed <------ p="" verb="">
6 7 me ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">

SENTENCE 78 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 was pushing <------ p="" verb="">

SENTENCE 79 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 had pushed <------ p="" verb="">

SENTENCE 80 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 would push <------ p="" verb="">

SENTENCE 81 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 could push <------ p="" verb="">

SENTENCE 82 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 should push <------ p="" verb="">

SENTENCE 83 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 7 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 7 shall push <------ p="" verb="">

SENTENCE 84 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase="">
1 2 visited <------ p="" verb="">
2 3 him ||obliq. pron.<----- accusative="" nbsp="" obliq.="" p="" pron.="">
3 6 who <-----------relative clause="" p="">
4 5 i <-----subject noun="" p="" phrase="">
5 6 pushed <------ p="" verb="">

SENTENCE 85 -------------------->ENGLISH PARSER OUTPUT
0 1 i <-----subject noun="" p="" phrase=""> 1 2 visited
<------ p="" verb=""> 2 3 him ||obliq. pron.
<----- accusative="" nbsp="" obliq.="" p="" pron.=""> 3 6 who
<-----------relative clause="" p=""> 4 5 i
<-----subject noun="" p="" phrase=""> 5 6 push
<------ p="" verb=""> SENTENCE 86 -------------------->ENGLISH PARSER OUTPUT 0 1 i


<-----subject noun="" p="" phrase=""> 1 2 visited
<------ p="" verb=""> 2 3 him ||obliq. pron.
<----- accusative="" nbsp="" obliq.="" p="" pron.=""> 3 7 who
<-----------relative clause="" p=""> 4 5 i
<-----subject noun="" p="" phrase=""> 5 7 can push
<------ p="" verb=""> SENTENCE 87 -------------------->ENGLISH PARSER OUTPUT 0 1 i


<-----subject noun="" p="" phrase=""> 1 2 visited
<------ p="" verb=""> 2 3 him ||obliq. pron.
<----- accusative="" nbsp="" obliq.="" p="" pron.=""> 3 7 who
<-----------relative clause="" p=""> 4 5 i
<-----subject noun="" p="" phrase=""> 5 7 may push
<------ p="" verb=""> SENTENCE 88 -------------------->ENGLISH PARSER OUTPUT 0 1 i


<-----subject noun="" p="" phrase=""> 1 2 visited
<------ p="" verb=""> 2 3 him ||obliq. pron.
<----- accusative="" nbsp="" obliq.="" p="" pron.=""> 3 7 who
<-----------relative clause="" p=""> 4 5 i
<-----subject noun="" p="" phrase=""> 5 7 will push
<------ p="" verb=""> SENTENCE 89 -------------------->ENGLISH PARSER OUTPUT 0 1 i


<-----subject noun="" p="" phrase=""> 1 2 visited
<------ p="" verb=""> 2 3 him ||obliq. pron.
<----- accusative="" nbsp="" obliq.="" p="" pron.=""> 3 7 who
<-----------relative clause="" p=""> 4 5 i
<-----subject noun="" p="" phrase=""> 5 7 am pushing
<------ p="" verb=""> SENTENCE 90 -------------------->ENGLISH PARSER OUTPUT 0 1 i


<-----subject noun="" p="" phrase=""> 1 2 visited
<------ p="" verb=""> 2 3 him ||obliq. pron.
<----- accusative="" nbsp="" obliq.="" p="" pron.=""> 3 7 who
<-----------relative clause="" p=""> 4 5 i
<-----subject noun="" p="" phrase=""> 5 7 have pushed
<------ p="" verb="">

Saturday, 17 September 2016

LANGANA ENGLISH TO TURKISH TRANSLATOR

LANGANA ENGLISH TO TURKISH TRANSLATOR


Ali Riza SARAL / ÇİZGİ AŞ.




ABSTRACT
LANGANA is an English to Turkish translation framework that can be extended to translate any
English sentence automatically.

INTRODUCTION
There are many programs on the internet that claim to translate English to Turkish correctly.
These  programs use statistical methods[1] to solve the translation problem.  Unfortunately,
every and each of these translation programs fail in many cases.  LANGANA is an English to
Turkish translation program that uses rule-based methods and parsing.
NOMENCLATURE
Translation engine, parser, language processing, English to Turkish translation


There are many programs on the internet that claim to translate English to Turkish correctly.
These  programs use statistical methods [1] to solve the translation problem.  Unfortunately,
every and each of these translation programs fail in many cases.

LANGANA is an English to Turkish translation program that uses rule-based methods and parsing.
This project is still in the feasibility phase.  This means it cannot guarantee a correct
translation of any English text.  It can correctly translate any English text which is similar
to the examples it has been developed for.  It may also do translations out of the range of
these examples due to the builtin flexibility of the languages.  Feasibility indicates
LANGANA may be extended to translate any new example case, a prototype sentence.

Translation from English to Turkish has many facets.  The English text must be parsed to
extract words and punctuations.  Then, the word types must be identified.  LANGANA uses
a Webster dictionary based, wordtype dictionary.  This wordtype dictionary is created
by parsing the Webster dictionary.  Wordtypes are determined according to their context
in the sentence structure.  A parsing algorithm is used to check the correctness of
the produced type.

The translation from English to Turkish requires to find the common minimum items.
For ex.   'the old teacher' is translated as 'yaşlı öğretmen'.  The word sequence
in these two examples are the same.  The translation is done word by word.

According to this, to prepare for the translation LANGANA identifies the word groups
such as noun phrase, preposition phrase  etc.

*******************************************************************

---------------------------------------------------
 He decided to telephone Mrs Jackson, who he had read about in the newspaper
--------------------------------------------------

        0  he ||nom. pron.  SUBJECT0      
0 1 he <-----subject noun="" p="" phrase="">
               1 S decided ||+reg. v.||v. t.||imp. & p. p. Decided||p. pr. & vb. n. Deciding||v. i.||v. t.||v. i.  VERB0

2 S to ||to-inf.  INFINITIVE TO
3 S telephone ||+reg. v.||v. t.  INFINITIVE VERB

                    4 S mrs ||pn.                    
                    5 S jackson ||pn.                    
4 6 mrs jackson <-----object after="" infinitive="" noun="" p="" phrase="">
6 ,S who ||rel. pron.
6 14 who <-----------relative clause="" comma="" p="" with="">
        7 S he ||nom. pron.  SUBJECT0      
7 8 he <-----subject noun="" p="" phrase="">
               8 S had ||+irreg. v. neutral||v. t.||imp. & p. p. Had||p. pr. & vb. n. Having  VERB0
               9 S read ||+irreg. v. neutral||+irreg. v. imp.||+irreg. v. participle||v. t.||p. pr. & vb. n. Reading  VERB0

                    10 S about ||+prep.  PREPOSITION PHRASE BEGINNING
11 S in ||+prep.
                    12 S the ||+def. art.                    
                    13 S newspaper ||n.                    
10 14 about in the newspaper <-----preposition p="" phrase="">
*******************************************************************

The functions of the words as subject, object, verb etc. are determined in the next phase.
This produces the English Parser output.

*******************************************************************

SENTENCE 0 -------------------->ENGLISH PARSER OUTPUT
0 1 he <-----subject noun="" p="" phrase="">
1 2 decided <------ p="" verb="">
2 4 to telephone  <-------------infinitive p="" verb="">
4 6 mrs jackson <-----object after="" infinitive="" noun="" p="" phrase="">
6 14 who <-----------relative clause="" comma="" p="" with="">
7 8 he <-----subject noun="" p="" phrase="">
8 10 had read <------ p="" verb="">
10 14 about in the newspaper <-----preposition p="" phrase="">
*******************************************************************

The English Parser output may be produced  seperately for related purposes.

The English to Turkish translation requires not only the translation of words,
phrases etc. but also the word sequence of the sentence.  In fact, the translation
of even punctuation is necessary in clause sentences.

LANGANA uses a dedicated section to do the word sequnce translation.
It translates the sentence structure based on positive, negative, question,
interrogative, imperative cases and some of their combinations.

Sequence translation is done for the main part of the sentence first.
Then the clause section's word sequence is translated.  A seperate structure
is used to keep the output Turkish word sequence:

*******************************************************************

getEngStructLength=8
------------------------------------>setTranslationSEQforConjunction 5 8
EngStruct==>0 = 0 1 he <-----subject noun="" p="" phrase="">EngStruct==>3 = 4 6 mrs jackson <-----object after="" infinitive="" noun="" p="" phrase="">EngStruct==>2 = 2 4 to telephone  <-------------infinitive p="" verb="">EngStruct==>1 = 1 2 decided <------ p="" verb="">EngStruct==>4 = 6 14 who <-----------relative clause="" comma="" p="" with="">EngStruct==>5 = 7 8 he <-----subject noun="" p="" phrase="">EngStruct==>7 = 10 14 about in the newspaper <-----preposition p="" phrase="">EngStruct==>6 = 8 10 had read <------ p="" verb="">
---------------------------------------------->setTranslationSEQforConjunction 0 8

relClauseInsertPOS=1
EngStruct==>0 = 0 1 he <-----subject noun="" p="" phrase="">EngStruct==>5 = 7 8 he <-----subject noun="" p="" phrase="">EngStruct==>7 = 10 14 about in the newspaper <-----preposition p="" phrase="">EngStruct==>6 = 8 10 had read <------ p="" verb="">EngStruct==>4 = 6 14 who <-----------relative clause="" comma="" p="" with="">EngStruct==>3 = 4 6 mrs jackson <-----object after="" infinitive="" noun="" p="" phrase="">EngStruct==>2 = 2 4 to telephone  <-------------infinitive p="" verb="">EngStruct==>1 = 1 2 decided <------ p="" verb="">
*******************************************************************

Translation of the verbal part is done in two phases.
The first phase translates the word, phrases etc. units word by word.

*******************************************************************

translation is ENABLED
translateEngTOTurk----------TRANSLATION PHASE----->

:
0 1 he <-----subject noun="" p="" phrase="">o
:
7 8 he <-----subject noun="" p="" phrase="">
:
10 14 about in the newspaper <-----preposition p="" phrase="">dahili gazete hakkında
:
8 10 had read <------ p="" verb="">okumuştu
:
6 14 who <-----------relative clause="" comma="" p="" with=""> olduğu
:
4 6 mrs jackson <-----object after="" infinitive="" noun="" p="" phrase="">Bayan jackson
:
2 4 to telephone  <-------------infinitive p="" verb="">telefon etme
:
1 2 decided <------ p="" verb="">karar verdi

*******************************************************************

The second phase produces and corrects the word extensions.

*******************************************************************

translateEngTOTurk-----------TRANSLATION PHASE 2nd PROCESSING----->

==================>0 1 he <-----subject noun="" p="" phrase="">
==================>7 8 he <-----subject noun="" p="" phrase="">
==================>10 14 about in the newspaper <-----preposition p="" phrase="">
==================>8 10 had read <------ p="" verb="">
==================>6 14 who <-----------relative clause="" comma="" p="" with="">
==================>4 6 mrs jackson <-----object after="" infinitive="" noun="" p="" phrase="">
++++++OBJ NOUN PHRASE---->Bayan jackson
findFragment fr==0
verbPOS engStruct[j]= decided=1
verbPOS+1 engStruct[j+1]= to telephone=2
infVB exists =telephone
found inf-verb=telephone
found verb=decided
OBJ in infinitive phrase verbOfOBJ==infVerbOfObject= telephone
verbOfOBJ=telephone objDIR=-e
objDIR of OBJECT NOUN PHRASE=-e
Bayan jacksona
==================>2 4 to telephone  <-------------infinitive p="" verb="">
telefon etme
found verb=decided
decide
-e
telefon etmeye
==================>1 2 decided <------ p="" verb="">
*******************************************************************

The final translation is output below.

*******************************************************************

----------------------------------------------------TÜRKÇE
 He decided to telephone Mrs Jackson, who he had read about in the newspaper.

 O dahili gazete hakkında okumuş olduğu Bayan jacksona telefon etmeye karar verdi.

Done
Done

As you might notice the verb phrase 'read about' is translated wrong and this
causes further complications of the preposition ‘in’.  The phrasal expressions
formed by more than one words are not handled yet.  These include verb + preposition,
multiple word verbs, multiple word adjectives, multiple word prepositions  etc.

Currently, LANGANA at the translation phase of relative clauses ('who' is almost finished),
and subjunctive conjunction, correlative conjunctions, their English parse phases are
completed.

After that an English to Turkish dictionary improvement is urgent.  Webster improvement,
phrase processing  will follow.

ACKNOWLEDGMENTS
Birol BAŞARAN, Ali Tamer ÜNAL, Mehmet Niyazi SARAL have provided financial and intellectual
support by ordering related projects.

REFERENCES
[1] Yandex School of Data Analysis
Russian-English Machine Translation System for WMT14
Alexey Borisov and Irina Galinskaya
Yandex School of Data Analysis
16, Leo Tolstoy street, Moscow, Russia
Proceedings of the Ninth Workshop on Statistical Machine Translation, pages 66–70,
Baltimore, Maryland USA, June 26–27, 2014.
[2] Purdue OWL https://owl.english.purdue.edu/owl
[3] British Council
https://learnenglish.britishcouncil.org/en
[4] 5000 test sentences regression test outputs https://sourceforge.net/projects/turkishlanguageparser/files/English%20to%20Turkish%20Translation%20Engine/20160605ARS%20English%20Translator%20Regression%20Test%205000%20test%20sentences%20_%20translation.txt/download
[5] English Parser outputs https://sourceforge.net/projects/turkishlanguageparser/files/English%20Language%20Syntax%20Parser/
[6] LANGANA testcases for Correlational Conjunctions in comparison with YANDEX http://tekne-techne.blogspot.com.tr/2016/03/langana-testcases-for-correlational.html

Sunday, 17 July 2016

LANGANA - YANDEX Relative Clause Comparison Initial Results


LANGANA                                                            YANDEX
----------------------------------------------------------------------------------------------
The book, which is lost, was found in the library.
Kayıp edilen kitap kütüphanenin içinde bulundu.    Kayıp kitap, kütüphanede bulundu.


The house that Jack built is large. 
Ev inşa eden Jack büyük.


The library had the book that I wanted.
Kütüphane istediğim kitaba sahip oldu.      Kütüphane istediğim bir kitap vardı.


Is the old lady who lives next door, a teacher?
Gelecek kapıya yaşıyan yaşlı hanımefendi bir öğretmen midir? Yandaki, bir öğretmen yaşayan yaşlı kadın mı?


Please see those people who we met on holiday.
Tatilin üstünde karşılaştığımız şu insanları lutfen görünüz.  Tatilde tanıştığımız bu insanlar bakın.


She was the woman who you were talking to.
O ona konuşuyor olduğun kadındı.    Kiminle konuşuyordun kadındı.


The woman who we do not speak to is my teacher.
Ona konuşmadığımız kadın benim öğretmenimdir.   Konuşmak yok olan kadın öğretmenim.


Who was not the woman who you were talking to?
Ona konuşuyor olduğun kadın kim değildi?   Kim kiminle konuşuyordun kadın değil miydi?

Thursday, 17 March 2016

LANGANA testcases for Correlational Conjunctions in comparison with YANDEX

This is a list of testcases that I will implement for LANGANA automatic translation program's CORRELATIONAL CONJUNCTIONS implementation.


The odd lines indicate the English testcase, whereas the even lines indicate the YANDEX translation to Turkish.  Approx 165 out 210 solutions of YANDEX are wrong.


Please be carefull about the completely wrong translations that change the positive meaning to negative or vise versa.  The correct translations are limited to basic sentences mostly used for checking purposes.


You either do your work or prepare for a trip to the office.
 Ya işini yap ya da ofisine bir gezi için hazır olun.
It changes the distribution of charges on either side of the membrane.
 Zarın her iki tarafındaki yüklerin dağılımını değiştirir.
It is transmitting a depolarization signal equally well from either cell.
ERROR Eşit derecede iyi ya da hücresinden nöron bir sinyal gönderiyor.
It touches the surface of either one of the two fused digits.
ERROR İki yuvarlak rakam ya da yüzeyine temas.
The rows of particles on either side of the active zone are strong.
ERROR Aktif bölgenin her iki tarafında parçacıkların satır güçlü.
This blocker may be either Magnesium or various organic polyamines.
ERROR Bu engelleyici ya da Magnezyum ya da çeşitli organik poliaminler olabilir.
Do neurotransmitters act either directly or indirectly on ion channels?
ERROR Ne nörotransmitter hareket doğrudan ya da dolaylı olarak üzerinde iyon kanalları?
It can produce either excitation or inhibition.
 Ya uyarma ya da inhibisyon üretebilir.
However, either the G protein or the second messenger can act directly on an ion channel.
ERROR Ancak, her iki G protein ya da ikinci haberci doğrudan iyon Kanalı üzerinde hareket edebilir.
They can mediate either excitatory or inhibitory actions in postsynaptic cells.  
ERROR Postsinaptik hücreleri ya da uyarıcı veya inhibitör eylemleri aracılık edebilirler.
They mediate either excitatory or inhibitory potentials.
ERROR Ya uyarıcı veya inhibitör potansiyeli aracılık ettiler.
There are still others with either an accelerating or decelerating train of action potentials.
ERROR Hala aksiyon potansiyeli ya hızlanıyor ya da yavaşlıyor tren ile başkaları da var.
Note that axodendritic synapses can occur on either the main shaft of a dendrite branch or on a specialized input zone.
ERROR Axodendritic sinapslar da dendrit bir şube ana şaft veya özel giriş bölgesi üzerinde oluşabilir unutmayın.
These are the changes in the amount of transmitter released due to either changes within the presynaptic terminal or extrinsic factors.
ERROR Bu değişiklikler nedeniyle de presinaptik terminal veya dış faktörler içinde taburcu verici miktarı değişiyor.
They are usually active against either of two peptide sequences.
ERROR Genellikle karşı iki peptid dizilerinin hepsi aktif.
These nuclei are located either on the midline of the thalamus or within the internal medullary lamina.
ERROR Bu çekirdekler talamus orta hat ya da iç dokusu tabakası içinde yer alır.
They often associate with either G protein-coupled or tyrosine kinase receptors.
ERROR Sık sık da G-protein eşli ya da tirozin kinaz reseptörleri ile ortak.
Upon entry the axon forms branches that either terminate within the spinal gray matter or ascend to nuclei.
ERROR Girişte akson ya da çekirdek spinal gri Madde veya yükselmek içinde sona erdirmek dalları oluşturur.
Receptor activation causes the cell either to depolarize or to hyperpolarize.
ERROR Reseptör aktivasyonu hücre ya da nötralize etmeye veya hyperpolarize neden olur.
Peptides with either related or antagonistic functions can be generated from the same precursor.
ERROR İle ilgili veya antagonist fonksiyonları ile peptitler aynı habercisi oluşturulabilir.
The subject can identify an image either verbally or by touching objects hidden behind the screen.
ERROR Konu bir resim tanımlamak ya sözlü olarak ya da ekranın arkasında gizli nesneleri dokunmadan.
A tactile stimulation either excites or inhibits a cell.
ERROR Dokunsal bir uyarım da heyecanlandıran ya da bir hücre engeller.
You must decide whether you stay or you go.
 Kal ya da git karar vermelisiniz.
Whether you stay or you go, the film must start at 8 pm.
ERROR Kalmak ya da gitmek ister, film 20: 00'de başlamalı.
I do not know whether he will come.
 O gelecek mi bilmiyorum.
It draws whether a horizontal or vertical pattern.
ERROR Yatay veya dikey bir desen olup olmadığını çekiyor.
I do not know whether it is right or wrong.
 Doğru ya da yanlış olup olmadığını bilmiyorum.
He gave the books whether to the students or their parents.
ERROR Öğrenciler veya aileleri için olsun kitaplar verdi.
It depends on whether the parents took the books or the students.
ERROR Ebeveynlerin kitapları veya öğrenci almaya bağlıdır.
Whether he knows the answer to my question or he is cheating, is not clear.
ERROR Sorumun cevabını biliyor olsun ya da şike mi yapıyor, belli değil.
Whether he studies hard or he looks like that, is not clear.
ERROR O sıkı ders çalışıyor ya da bunun gibi mi, belli değil.
Whether he is a student or a teacher is not clear.
 Bir öğrenci ya da öğretmen olup olmadığı belli değil.
I do not know whether he is a teacher or looks like a teacher.
 O bir öğretmen ya da öğretmen gibi olup olmadığını bilmiyorum.
I do not know whether he comes from Ankara or from Istanbul.
ERROR Ankara'dan ya da İstanbul'dan geliyor bilmiyorum.
She does not know whether to push or pull.
ERROR İtme veya çekme olup olmadığını bilmiyor.
I do not know whether her eyes are green or blue.
ERROR Gözleri yeşil ya da mavi olup olmadığını bilmiyorum.
Neurons discharge whether an object is grasped or bitten.
ERROR Bir nesneyi kavradı veya ısırılan olup olmadığını nöronların deşarj.
I do not know whether their eyes are open or closed.
ERROR Gözleri açık ya da kapalı olup olmadığını bilmiyorum.
It works without addressing whether each of us sees the same blue.
ERROR Her birimiz aynı mavi görür mü ele almadan çalışıyor.
It depends on whether the animal is paying attention to the stimulus.
ERROR Hayvanın uyarana dikkatini bağlıdır.
It evaluates whether a stimulus is present.
 Bir uyarıcı olup olmadığını değerlendirir.
It is not certain whether their projections contribute significantly to the analysis of sound.
ERROR Projeksiyonları önemli ölçüde ses analizlerine katkıda bulunmak, belli değil.
It determines whether we see a vertical or horizontal pattern.
ERROR Dikey ya da yatay bir eğilim görürüz olup olmadığını belirler.
The subject is instructed to identify whether an item is present or not.
ERROR Konu bir madde mevcut olup olmadığını belirlemek için talimat verdi.
The monkeys indicated whether the gratings were vertical or horizontal.
ERROR Bu kafesler dikey ya da yatay olup olmadığını maymunlar belirtti.
It is not known whether each cell responds to one tastant or a combination of tastants.
ERROR Her hücre bir tastant veya tastants bir arada yanıt bilinmemektedir.
It depends on whether the primary perturbation is to the feet or the head.
ERROR Birincil kökler ayak ya da baş olmasına bağlıdır.
The answer depends on whether individual parameters are specified in successive stages of processing or in parallel pathways.
ERROR Cevap bireysel parametreleri işleme ardışık aşamalarında veya paralel yolları belirtilmedi bağlıdır.
Whether these periods are ancestral to mammalian sleep or simply species-specific forms of rest is not clear.
ERROR Bu süreler memeli uyku ya da sadece türün atası olup-dinlenme biçimleri belli değil.
The premotor area is active whether the monkey performs a task or observes someone else perform the task.
ERROR Maymun bir görevi yerine getirir ya da başka birisi bu görevi gerçekleştirmek gözlemler olsun premotor alanı aktif.
Excitation of a particular sensory neuron, whether naturally or artificially by direct electrical stimulation, elicits the same sensation.
 Belirli bir duyusal nöron uyarma, ister doğal veya yapay olarak doğrudan elektrik stimülasyonu ile, aynı hissi ortaya çıkarır.  <---- br="">Steven Keele first sought to determine whether this defect results from a motor deficit or from a more fundamental defect in the timing of serial events.
ERROR Steven Keele ilk bu arıza motor açık " ya da seri olayların zamanlaması daha temel bir kusur sonuçlar olup olmadığını belirlemek için çalıştı.
He determined whether the injury has occurred within the brain or further along the course of the nerve.
ERROR Yaralanma, beyin veya daha fazla içindeki sinirin seyri boyunca oluşup oluşmadığını tespit etti.
The answer depends on whether individual parameters are specified in successive stages of processing or in parallel pathways.
ERROR Cevap bireysel parametreleri işleme ardışık aşamalarında veya paralel yolları belirtilmedi bağlıdır.
This happens depending on whether ipsilateral or contralateral nerves were stimulated.
ERROR Bu ipsilateral veya kontralateral sinir uyarıldı bağlı olarak gerçekleşir.
Most deafness, whether mild or profound, falls into this category.             
ERROR En sağırlık, ister hafif ya da derin, bu kategoriye girer.
It depends on whether the hair cell is respectively depolarized or hyperpolarized from its resting potential.
ERROR Saç hücre sırasıyla daha az veya dinlenme potansiyelinden hiperpolarize olmasına bağlıdır.
Neuron discharges whether an object is grasped or bitten.
ERROR Bir nesneyi kavradı veya ısırılan olup olmadığını nöronun deşarj.
It depends on whether their eyes are open or closed.
 Gözleri açık veya kapalı olmasına bağlıdır.
He is not only handsome, but also brilliant.
ERROR Sadece yakışıklı ve geleceği parlak değil, ama aynı zamanda.
He has gone not only to Ankara, but also to Istanbul.
ERROR İstanbul'a gitti Ankara için değil sadece, aynı zamanda var.
Not only is he handsome, but also he is brilliant.
 Yakışıklı olmasının dışında, aynı zamanda çok zekidir.
The teacher not only asked difficult questions, but also helped the students.
ERROR Öğretmen zor sorular değil sadece, aynı zamanda öğrencilere yardımcı oldu.
His questions are different from standard questions not only in difficulty but also in depth.
ERROR Sorularını zorluk değil, aynı zamanda derinlemesine standart sorular farklıdır.
He reads not only books but also newpapers.
ERROR Sadece kitap okuyor ama aynı zamanda Gazeteler.
I must study not only science but also mathematics.
 Sadece bilim değil, aynı zamanda matematik çalışmak zorundayım.
Science is not only difficult but also extensive.
 Bilim zor ama aynı zamanda geniş kapsamlıdır.
Science not only explains the world but also helps the people.
ERROR Bilim dünyasına açıklar, hem de insanlara yardım ediyor.
Not only are reading and listening processed separately, but the act of thinking about a word's meaning activates a still different area in the left frontal cortex.
ERROR Sadece okuma ve ayrı ayrı işlenmiş dinliyor, ama bir kelime anlamını düşünerek hareket sol frontal korteks hala farklı bir alanı harekete geçirir.
Aphasia patients not only manifest cognitive defects in language, but also have trouble with the affective aspects of language.
ERROR Dilinde afazi hastalar sadece apaçık bilişsel kusurlar, ama aynı zamanda dilin duygusal açıdan sorun var.
These channels not only depolarize the presynaptic cell above the threshold, they must also generate sufficient ionic current in the postsynaptic cell.
ERROR Bu kanallar sadece eşiğin presinaptik hücreyi depolarize değil, aynı zamanda postsinaptik hücrede yeterli iyonik akımı üretmek gerekir.
It is determined not only by the voltage across the membrane but also by the ionic concentration gradients.
ERROR Belirlenen membran arasındaki gerilimi değil, aynı zamanda iyonik konsantrasyon geçişlerini.
The proper function of a protein is defined not only by its primary amino acid sequence, but also by its secondary and tertiary structure.
ERROR Protein düzgün tanımlı birincil amino asit dizisi tarafından değil, aynı zamanda ikincil ve üçüncül yapısı gereğidir.
The proper function of these proteins depends not only on their primary amino acid sequence, but also on correct folding.
ERROR Bu proteinlerin düzgün bağlı sadece birincil amino asit dizisi üzerinde değil, ama aynı zamanda doğru katlanır.
Neurons in the neocortex are not only distributed in layers but also in columns.
ERROR Neokorteks nöronlar tabakalar halinde de sütunlara dağıtılmış ama değil sadece.
Similar interactions are important for exocytosis in all cells, not only in the synaptic terminals of neurons.
ERROR Benzer etkileşimler hücre hücre, nöronlar sadece sinaptik terminaller için önemlidir.
A third important difference is that metabotropic synaptic actions can not only increase channel opening, they can also decrease channel opening.
ERROR Metabotropik sinaptik eylemleri sadece kanal açma artırabilir üçüncü önemli bir fark var, ayrıca kanal açma azaltabilir.
The thalamus not only projects to the visual areas of the neocortex but also receives a return projection from the neocortex.
ERROR Talamus neokorteks görsel alanları için de neokorteks bir dönüş projeksiyon aldığı projeler.
Prefrontal neurons not only remember particular places within the visual field but do so in order to guide eye movements to those places.
ERROR Prefrontal nöronların sadece görme alanı içinde belirli yerler hatırlamıyor ama o yerlere göz hareketleri rehberlik yapmak.
Our perceptions not only appear whole for the instant of the experience, but they appear to be whole and continuous over time.
ERROR Algılarımız deneyimi an için görünen tüm değil sadece, ama zaman içinde bütün ve sürekli olarak görünürler.
Second messengers not only can modify preexisting proteins, but also can induce the synthesis of new proteins by altering gene expression.
ERROR İkinci haberci proteinleri önceden değiştirebilirsiniz değil sadece, aynı zamanda gen ifadesini değiştirerek yeni protein sentezi teşvik edebilir.
The real resting membrane has open channels not only for Na+ and K+, but also for Cl-.
ERROR Gerçek dinlenme membran açık kanalları var+ ve K+ Na için değil sadece, aynı zamanda Cl için.
The distribution of specific types varies not only from cell to cell but also from region to region within a cell.
ERROR Belirli türde bir dağıtım hücre içinde değişir hücreden hücreye bölge için değil sadece, aynı zamanda bölge.
We must understand not only the properties of individual cells and pathways but also the network properties of functional circuits in the brain.
ERROR Beyindeki işlevsel devreler sadece bireysel hücreler ve yolların özellikleri, ama aynı zamanda ağ özelliklerini anlamak gerekir.
This requires not only the methods and approaches of cellular and systems neuroscience but also the insights of cognitive psychology. 
ERROR Bu bilişsel psikoloji sadece hücresel ve sistemleri nörobilim yöntemleri ve yaklaşımları aynı zamanda anlayış gerektirir.
Such neural maps reflect not only the position of receptors but also their density.
ERROR Bu nöral haritalar reseptörleri aynı zamanda yoğunluk onların sadece pozisyon değil yansıtır.
Neither the basketball team nor the football team is doing well. 
 Ne Basketbol Takımı, ne de futbol takımı iyi gidiyor.
Neither his words were true.
ERROR Ne sözleri doğruydu.
Neither parents nor students came to the meeting.
 Ne veliler ne de öğrenciler toplantıya geldi.
Neither has he studied English nor he has studied mathematics.
ERROR İkisi de İngilizce öğreniyor etti ne de matematik okudu.
The tired students could not concentrate nor they could answer the questions.
ERROR Yorgun öğrenciler konsantre olamıyor, ne de sorulara yanıt verebilir.
The tired students could not concentrate nor could answer the questions.
ERROR Yorgun öğrenciler konsantre olamıyor ne de sorulara cevap verebilir.
He both prepared for the science exam and took the other exams.
 Hem de bilim sınavı için hazırladığı ve diğer sınavlar aldı.
The examination of the comatose patient is neither difficult nor time-consuming.
ERROR Komadaki hastanın muayenesi de oldukça zaman alıcıdır.
If the stimulus is neither beneficial nor harmful, the animal learns.
 Eğer uyarıcı ne yararlı ne de zararlı ise, hayvan öğrenir.
Some areas of the brain are neither purely sensory nor purely motor but instead are modulatory
ERROR Beynin bazı bölgeleri de tamamen duyusal ne de tamamen motor var ama onun yerine düzenleyici var
However, neither of these two aminergic pathways is essential for locomotion.
ERROR Ancak, ne bu iki aminergic yollarının hareket için gereklidir.
In simple recessive disease neither parent may have the disease.
ERROR Basit resesif hastalık ebeveynleri hastalık olabilir.
Neither the patients nor the control subjects could scale their response to the random displacement trials.
ERROR Ne hasta ne de kontrol denekleri rastgele yer değiştirme denemelere tepki ölçekli olabilir.
Neither has he studied English nor he has studied mathematics.
ERROR İkisi de İngilizce öğreniyor etti ne de matematik okudu.
Affected patients can neither sense the motions of their joints nor detect objects touching their fingers.
ERROR Etkilenen hastalar ne eklem hareketleri mantıklı ne de parmaklarını dokunarak nesneleri algılayabilir.
Both the cross country team and the swimming team are doing well.
 Hem kros takımı ve yüzme takımı iyi gidiyor.
Teachers and students must both be successful.
ERROR Öğretmenler ve öğrenciler, hem başarılı olması gerekir.
Both must be successful.
 Her ikisi de başarılı olması gerekir.
Both teachers and students must be successful.
ERROR Öğrenci ve öğretmenlere başarılı olması gerekir.
Both types of questions are difficult.
ERROR Sorular her iki tür zordur.
Both of these are difficult to explain.
ERROR Bunların her ikisi de açıklamak zordur.
Both of these questions are difficult to explain.
ERROR Bu soruların her ikisine de açıklamak zordur.
Both questions are difficult.
 İki sorunun da cevabı zordur.
He failed in both examinations.
ERROR Hem sınavlarında başarısız oldu.
Diligence intelligence are both necessary to be successful in the examinations.
ERROR Çalışkanlık istihbarat hem de sınavlarda başarılı olmak için gereklidir.
Success helps the student both in the school and in the public life.
ERROR Başarı her iki okulda ve kamu hayatında öğrenci olur.
The exam tests the student both intellectually and diligently.
ERROR Sınav hem entelektüel ve özenle öğrenci testleri.
Some excitatory, some inhibitory, some both are activated together.
 Bazıları uyarıcı, bazıları inhibitör, bazısı da her ikisi birlikte etkinleştirilir.
Tetrodotoxin and saxitoxin both bind to Na+ channels with a very high affinity.
ERROR Tetrodotoxin ve saxitoxin hem Na çok yüksek bir afinite ile Kanalları+ bağlama.
Both can be approximated by a short circuit.
ERROR Her ikisi de kısa devre edilmiş olabilir.
Both sensory and motor neurons were identified by injection.
ERROR Her ikisi de duyusal ve motor nöron enjeksiyon tarafından tespit edildi.
Both types of motor neurons are located in the ventral horn of the spinal cord.
ERROR Motor nöron her iki tür omuriliğin ventral boynuz bulunur.
This relates both of these to an organism's behavior.
ERROR Bu, bir organizmanın davranış için bunların her ikisi de ilgilidir.
If present in both copies of a gene, the result is positive.
ERROR Eğer bir genin iki kopyası varsa, sonuç olumlu.
Heredity and environment are both necessary for the expression of phenylketonuria.
ERROR Kalıtım ve çevre her iki faktör olabileceği ifade için gereklidir.
This is a representation of language by both auditory and visual inputs.
 Bu hem işitsel ve görsel girişleri ile dilin bir temsili.
The reading of a single word produces a response both in the primary visual cortex and in the visual association cortex.
ERROR Tek bir kelime okuma birincil görsel korteks görsel dernek korteks hem de bir tepki üretir.
The sensory neurons make both excitatory connections and connections through inhibitory interneurons.
ERROR Duyusal nöronlar inhibitör internöron ile hem uyarıcı bağlantıları ve bağlantıları yapın.
It increases both the speed and reliability of function within the central nervous system.
ERROR Merkezi sinir sistemi içinde işlev hem hız ve güvenilirliği arttırır.
There are many systems with both serial and parallel components.
ERROR Her iki seri ve paralel bileşenleri ile pek çok sistemleri vardır.
The housekeeping genes are highly conserved, in both number and structure.
ERROR Housekeeping genler yüksek oranda korunmuş, hem sayı ve yapısında vardır.
Thus both visual and auditory pathways converge on Broca's area.
ERROR Böylece hem görsel hem de işitsel yolları Broca bölgesinde bir sorun var.
manic-depressive disorder and manic disorder are thought to represent a variety of disorders both etiologically and genetically.
ERROR manik-depresif bozukluk ve manik bozukluk etyolojik ve genetik bozukluklar çeşitli temsil düşünülmektedir.
He is talented both intellectually and diligently.
ERROR Hem entelektüel ve özenle yetenekli.
He has both diligent and powerful talents.
ERROR Her ikisi de çalışkan ve güçlü yetenekleri var.
Football is as fast as hockey.
 Futbol, hokey gibi hızlı.
Football is as much an addiction as a sport.
 Futbol bir spor olduğu kadar bir bağımlılıktır.
The teacher imagined the exam as a challenge.
ERROR Öğretmen bir meydan okuma olarak sınav hayal.
School can not be represented as series of examinations.
ERROR Okul, sınavlar bir dizi olarak temsil edilebilir.
AS far as is known, the teacher migrated to USA.
 Bilindiği kadarıyla, öğretmen ABD'YE göç etmiş.
Some diseases such as Parkinson's disease are lethal.
 Parkinson hastalığı gibi bazı hastalıklar öldürücüdür.
I do not understand the system as a whole.
 Bir bütün olarak sistemi anlamadım.
She has become known as a terrific teacher.
 Müthiş bir öğretmen olarak bilinen haline gelmiştir.
Different questions serve as different challenges to the student.
 Farklı sorular öğrenci için farklı zorluklar olarak hizmet vermektedir.
As a result, he succeeded at the exam.
 Sonuç olarak, sınavda başarılı oldu.
He became increasingly successful as he studied more.
ERROR Daha okudu olarak giderek daha başarılı oldu.
As far as is known, glia are not directly involved.
 Bilindiği kadarıyla, glia doğrudan dahil değildir.
The first of these has become known as the principle of dynamic polarization.
 Bunlardan ilki dinamik kutuplaşma prensibi " olarak adlandırıldı.
Unipolar cells have a single process, with different segments serving as receptive surfaces or releasing terminals.
ERROR Tek kutuplu hücreler tek bir işlem var, açık yüzeyleri veya serbest terminali olarak hizmet veren farklı kesimleri ile.
The point at which two neurons communicate is known as a synapse.
ERROR İki nöron iletişim noktası sinaps olarak bilinir.
Knowledge is not stored as complete representations but rather is subdivided into distinct categories and stored separately.
 Bilgi tam olarak beyan saklı değil ama oldukça farklı kategorilere ayrılır ve ayrı olarak saklanır.
As a result, identical twins share all genes;
ERROR Sonuç olarak, tek yumurta ikizleri genlerinin payı;
As we shall learn below and in Chapter 7, this is not true.
 Aşağıda ve 7. Bölümde öğreneceğiz gibi, bu doğru değil.
The student succeeds as the teacher becomes successful.             
ERROR Öğretmen başarılı olur gibi öğrenci başarılı olur.
It may partially return as undamaged parts of the brain reorganize their linkages.
ERROR Kısmen beynin hasar görmemiş parça ilişkilerini yeniden düzenlemek olarak geri dönebilir.
Just as many Americans love basketball, so many Canadians love ice hockey.
ERROR Sadece birçok Amerikalı basketbol aşk gibi, pek çok Kanadalı buz hokeyi seviyorum.
This exists also in subordinate conjunctions.
ERROR Bu da alt Bağlaçlar var.
Just as grief is a normal response to personal loss, anxiety is a normal response to threatening situations.
ERROR Sadece keder kişisel kaybı normal bir tepki olarak, anksiyete tehdit eden durumlara normal bir tepki.
Just as there are distinct modalities of sensation, there are three distinct categories of cognition.
ERROR Sadece hissi farklı yöntemler olduğu gibi, biliş üç farklı kategoriler var.

There are three distinct categories of cognition, just as there are distinct modalities of sensation.
ERROR Sadece hissi farklı yöntemleri vardır biliş üç farklı kategoriler var.
Motor psychophysical studies of movement describe the relationships between intended actions and performance, just as sensory psychophysical studies relate physical stimuli to sensory experience in a quantitative way.
ERROR Sadece duyusal psikofizik çalışmaların nicel bir şekilde duyusal deneyim için fiziksel uyaranlara ilgili olarak hareketin Motor psikofizik çalışmaların hedeflenen eylemler ve performans arasındaki ilişkileri açıklar.
The brain can construct a good representation of the original information, just as the brain normally constructs conscious memory.
ERROR Beyin sadece beyin normalde bilinçli bir hafiza olarak özgün bilgi için iyi bir sunum yap.

Just as the cranial nerves are homologous to spinal nerves, so are the sensory and motor nuclei within the brain stem similar to those of the spinal cord.
ERROR Sadece kranial sinirler spinal sinirler yöntemdir, yani omurilik benzer beyin sapı içinde duyusal ve motor çekirdekleri vardır.

Just as schizophrenia is a multifactorial disease, so it is likely that antipsychotic drugs act on more than one molecular target.
ERROR Sadece şizofreni etkenli bir hastalık olduğu için, antipsikotik ilaçlar birden fazla moleküler hedef üzerinde hareket etmesi muhtemeldir.

It slowed and its beat diminished just as if its vagus had been stimulated.
ERROR Sanki vagus ile uyarılmış olsaydı yavaşladı ve yendi onun azalmış.
The more you practice writing, the better you will be at it.
ERROR Daha fazla yazı pratik yaparsan o kadar iyi olacaktır.
The greater the specialization area, the more books you have to read.
ERROR Okumak için daha fazla uzmanlık alanı, daha fazla kitap.
The more urgent issue is not this.
 Daha acil sorun bu değil.
The more distant the object, the smaller the eye movement.
ERROR Daha uzaktaki nesnenin daha küçük göz hareketi.
The more he studied, the more successful he got.
ERROR Daha fazla okumuş, daha başarılı buldu.
 The larger the area of a capacitor, the more charge it will store for a given potential difference.
ERROR Belirli bir potansiyel fark için depolar bir kondansatör daha büyük alanda, daha fazla ücret.
The more effective the substitution, the more readily ions can traverse the Na+ channel. 
ERROR Daha etkili yerine, daha kolay iyonları Na+ kanal geçiş.
The more K+ continues to flow, the more charge will be separated and the greater will be the potential difference.
ERROR Daha fazla K+ akışı devam ediyor, daha fazla ücret ikiye ayrılacak ve daha fazla potansiyel farkı olacaktır.
In this case of conjunctive search, the more items present, the longer the search takes.
ERROR Dinamik arama bu durumda, daha fazla öğe mevcut, uzun aramayı alır.
The more famous mistakes can be caught easily.
ERROR Daha ünlü hataları kolayca yakalandı.
Medication can be considered in the more severe cases.
 İlaçlar daha şiddetli durumlarda kabul edilebilir.
I found the more general solution.
 Daha genel bir çözüm buldum.
No sooner did she learn to ski, than the snow began to thaw.
ERROR Hayır er kar yumuşamaya başladı daha kayak yapmayı öğrenmek istedi.
No sooner had the warm liquid mixed with the crumbs touched my palate than a shudder ran through me and I stopped. 
ERROR Hiçbir er kırıntıları ile karışık sıcak sıvı bir ürperti bana koştu daha benim damak dokundu ve bıraktım.
I would rather swim than surf.
ERROR Daha doğrusu sörf daha yüzerdim.
I would rather go there than stay here.
 Burada kalmaktansa oraya giderdim.
Rather, they are diligent students.
ERROR Daha doğrusu, çalışkan öğrenci var.
Rather, they go to school.
ERROR Yerine, okula gidiyorlar.
Rather than going to school, he played football.
ERROR Aksine okula gitmek yerine, futbol oynadı.
He stayed at home, deciding to stop rather than to continue his education.
ERROR Evde kaldı, yerine eğitimine devam etmek için karar.
Freedom is not a game but rather is a privelege to enjoy.
ERROR Özgürlük bir oyun değil, zevk için bir privelege.
Knowledge is not stored as complete representations but rather is subdivided into distinct categories.
ERROR Bilgi tam olarak beyan saklı değil ama oldukça farklı alt kategoriye ayrılır.
They do not move about freely in the terminal but rather are restrained to a network.
ERROR Terminalde özgürce bir ağa bağlanmış olan değil, hareket etmiyor.
Rather, each behavior is generated by the actions of many cells.
ERROR Bunun yerine, her davranış, birçok hücre eylemleri tarafından oluşturulur.
He is a lazy student; rather, he works at the same time with school.
 Tembel bir öğrenci, daha ziyade, okul ile aynı anda çalışıyor.
He studies science, rather than social subjects.
ERROR Fen yerine sosyal konuları inceliyor.
He spoke with his hands rather than by sound.
ERROR Elleriyle yerine sesi daha konuştu.
I am tired of these rather lazy students.
ERROR Bu oldukça tembel öğrenciler bıktım.
The books are rather serious.
 Kitaplar oldukça ciddi.
He will be congratulated rather warmly.
ERROR Oldukça sıcak bir şekilde kutladı olacak.
He stopped working rather strikingly during the examination.
ERROR Oldukça çarpıcı muayene sırasında çalışmayı durdurdu.
It is continuous and low level rather than synchronous and concentrated.
ERROR Oldukça uyumlu ve konsantre daha sürekli ve düşük seviye.
Chemical activities should be used rather than concentrations.
ERROR Kimyasal faaliyetler oldukça Konsantrasyonu daha kullanılmalıdır.
The causative factor is therefore in the plasma, rather than a function of the lymphocytes themselves.
ERROR Etken bu nedenle plazma, daha ziyade kendilerini lenfositlerin bir işlevi daha.
All the successes as well as failures belong to us.
ERROR Tüm bu başarıların yanı sıra başarısızlıkların bize ait.
Our pleasures, joys, laughter and jests, as well as our sorrows, pains, griefs and tears arise from the brain.
ERROR Zevkler, sevinçler, kahkahalar ve Latife olarak dertlerimizi, acılar, keder ve gözyaşları bizim beyin ortaya çıkar.
We have to develop an appreciation of subtleties of language, such as irony, metaphor, and wit, as well as the emotional content of speech.
ERROR Dilin inceliklerini, ironi, konuşma, mecaz, ve zeka gibi, duygusal içerik olarak bir takdir geliştirmek zorundayız.
Benign mutations are allelic polymorphisms that produce differences in body type, such as eye color or hair color.
ERROR İyi huylu mutasyonlar vücut tipi farklılıkları, örneğin göz rengi veya saç rengi gibi üreten allelic polimorfizmi.
However, most behavioral traits as well as most common genetic disorders are multigenic.
 Ancak, çoğu davranış özellikleri yanı sıra en sık görülen genetik bozukluklar multigenic.
 His skill was known by his parents as well as by his teachers.
ERROR Beceri onun ailesinin yanı sıra öğretmenleri tarafından biliniyordu.
 He drives his intellectual power from his books as well as from his classes.
ERRORKitapları yanı sıra sınıflar, onun fikri, onun gücünü kullanıyor.
Thus language processing is parallel as well as serial.
ERROR Böylece dil işleme seri de paralel olarak.
Drosophila can produce abnormalities in learned as well as innate behaviors.
ERROR Drosophila öğrenmiş gibi doğuştan gelen davranışlar anormallikleri üretebilir.
Both disciplines recognize the importance of genetic as well as learned factors in determining behavior.
ERROR Her iki disiplinde davranışını belirleyen genetik olarak öğrenilmiş etkenlerin öneminin farkındayız.
Individual movements as well as complex motor actions derive from the patterns of firing of large networks.
ERROR Bireysel hareketler gibi karmaşık motor eylemler büyük ağlar, ateş kalıpları türetmek.
It is possible to distinguish gray and white matter as well as cerebrospinal fluid with high contrast.
ERROR Yüksek kontrastlı gri ve beyaz madde olarak beyin-omurilik sıvısı ayırt etmek mümkündür.