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reference code for syntaxnet

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syntaxnet

syntaxnet

description

test code for syntaxnet
- training and test a model using UD corpus.
- training and test a Korean parser model using the Sejong corpus.
- exporting a trained model and serving(limited to the designated version of syntaxnet(old one))
- training and test a model using dragnn.
- comparision to bist-parser.

history

2017. 3. 27

test for dragnn
version

python : 2.7
bazel  : 0.4.3
protobuf : 3.2.0
syntaxnet : 40a5739ae26baf6bfa352d2dec85f5ca190254f8

2017. 3. 10

modify for recent version of syntaxnet(tf 1.0), OS X(bash script), universal treebank v2.0
version

python : 2.7
bazel  : 0.4.3
protobuf : 3.0.0b2, 3.2.0
syntaxnet : bc70271a51fe2e051b5d06edc6b9fd94880761d5

2016. 8. 16
- add 'char-map' to context.pbtxt' for train
- add '--resource_dir' for test
  - if you installed old version of syntaxnet(ex, a4b7bb9a5dd2c021edcd3d68d326255c734d0ef0 ), you should specify path to each files in 'context.pbtxt'
- version
```
syntaxnet : a5d45f2ed20effaabc213a2eb9def291354af1ec
```

how to test

# after installing syntaxnet.
# gpu supporting : https://github.com/tensorflow/models/issues/248#issuecomment-288991859
$ pwd
/path/to/models/syntaxnet
$ git clone https://github.com/dsindex/syntaxnet.git work
$ cd work
$ echo "hello syntaxnet" | ./demo.sh
# training parser only with parsed corpus
$ ./parser_trainer_test.sh

univeral dependency corpus

UD official website
- tutorial
- CoNLL-U format
UPPipe
- udpipe(git)
prepare data

$ cd work
$ mkdir corpus
$ cd corpus
# downloading ud-treebanks-v2.0.tgz
$ tar -zxvf ud-treebanks-v2.0.tgz  
$ ls universal-dependencies-2.0 
$ UD_Ancient_Greek  UD_Basque  UD_Czech ....

training tagger and parser with another corpus

# for example, training UD_English.
# detail instructions can be found in https://github.com/tensorflow/models/tree/master/syntaxnet
$ ./train.sh -v -v
...
#preprocessing with tagger
INFO:tensorflow:Seconds elapsed in evaluation: 9.77, eval metric: 99.71%
INFO:tensorflow:Seconds elapsed in evaluation: 1.26, eval metric: 92.04%
INFO:tensorflow:Seconds elapsed in evaluation: 1.26, eval metric: 92.07%
...
#pretrain parser
INFO:tensorflow:Seconds elapsed in evaluation: 4.97, eval metric: 82.20%
...
#evaluate pretrained parser
INFO:tensorflow:Seconds elapsed in evaluation: 44.30, eval metric: 92.36%
INFO:tensorflow:Seconds elapsed in evaluation: 5.42, eval metric: 82.67%
INFO:tensorflow:Seconds elapsed in evaluation: 5.59, eval metric: 82.36%
...
#train parser
INFO:tensorflow:Seconds elapsed in evaluation: 57.69, eval metric: 83.95%
...
#evaluate parser
INFO:tensorflow:Seconds elapsed in evaluation: 283.77, eval metric: 96.54%
INFO:tensorflow:Seconds elapsed in evaluation: 34.49, eval metric: 84.09%
INFO:tensorflow:Seconds elapsed in evaluation: 34.97, eval metric: 83.49%
...

training parser only

# if you have other pos-tagger and want to build parser only from the parsed corpus :
$ ./train_p.sh -v -v
...
#pretrain parser
...
#evaluate pretrained parser
INFO:tensorflow:Seconds elapsed in evaluation: 44.15, eval metric: 92.21%
INFO:tensorflow:Seconds elapsed in evaluation: 5.56, eval metric: 87.84%
INFO:tensorflow:Seconds elapsed in evaluation: 5.43, eval metric: 86.56%
...
#train parser
...
#evaluate parser
INFO:tensorflow:Seconds elapsed in evaluation: 279.04, eval metric: 94.60%
INFO:tensorflow:Seconds elapsed in evaluation: 33.19, eval metric: 88.60%
INFO:tensorflow:Seconds elapsed in evaluation: 32.57, eval metric: 87.77%
...

test new model

$ echo "this is my own tagger and parser" | ./test.sh
...
Input: this is my own tagger and parser
Parse:
tagger NN ROOT
 +-- this DT nsubj
 +-- is VBZ cop
 +-- my PRP$ nmod:poss
 +-- own JJ amod
 +-- and CC cc
 +-- parser NN conj

# original model
$ echo "this is my own tagger and parser" | ./demo.sh
Input: this is my own tagger and parser
Parse:
tagger NN ROOT
 +-- this DT nsubj
 +-- is VBZ cop
 +-- my PRP$ poss
 +-- own JJ amod
 +-- and CC cc
 +-- parser ADD conj 

$ echo "Bob brought the pizza to Alice ." | ./test.sh
Input: Bob brought the pizza to Alice .
Parse:
brought VBD ROOT
 +-- Bob NNP nsubj
 +-- pizza NN dobj
 |   +-- the DT det
 +-- Alice NNP nmod
 |   +-- to IN case
 +-- . . punct

# original model
$ echo "Bob brought the pizza to Alice ." | ./demo.sh
Input: Bob brought the pizza to Alice .
Parse:
brought VBD ROOT
 +-- Bob NNP nsubj
 +-- pizza NN dobj
 |   +-- the DT det
 +-- to IN prep
 |   +-- Alice NNP pobj
 +-- . . punct

training parser from Sejong treebank corpus

# the corpus is accessible through the path on this image : https://raw.githubusercontent.com/dsindex/blog/master/images/url_sejong.png
# copy sejong_treebank.txt.v1 to `sejong` directory.
$ ./sejong/split.sh
$ ./sejong/c2d.sh
$ ./train_sejong.sh
#pretrain parser
...
NFO:tensorflow:Seconds elapsed in evaluation: 14.18, eval metric: 93.43%
...
#evaluate pretrained parser
INFO:tensorflow:Seconds elapsed in evaluation: 116.08, eval metric: 95.11%
INFO:tensorflow:Seconds elapsed in evaluation: 14.60, eval metric: 93.76%
INFO:tensorflow:Seconds elapsed in evaluation: 14.45, eval metric: 93.78%
...
#evaluate pretrained parser by eoj-based
accuracy(UAS) = 0.903289
accuracy(UAS) = 0.876198
accuracy(UAS) = 0.876888
...
#train parser
INFO:tensorflow:Seconds elapsed in evaluation: 137.36, eval metric: 94.12%
...
#evaluate parser
INFO:tensorflow:Seconds elapsed in evaluation: 1806.21, eval metric: 96.37%
INFO:tensorflow:Seconds elapsed in evaluation: 224.40, eval metric: 94.19%
INFO:tensorflow:Seconds elapsed in evaluation: 223.75, eval metric: 94.25%
...

#evaluate parser by eoj-based
accuracy(UAS) = 0.928845
accuracy(UAS) = 0.886139
accuracy(UAS) = 0.887824
...

test korean parser model

$ cat sejong/tagged_input.sample
1	프랑스	프랑스	NNP	NNP	_	0	_	_	_
2	의	의	JKG	JKG	_	0	_	_	_
3	세계	세계	NNG	NNG	_	0	_	_	_
4	적	적	XSN	XSN	_	0	_	_	_
5	이	이	VCP	VCP	_	0	_	_	_
6	ᆫ	ᆫ	ETM	ETM	_	0	_	_	_
7	의상	의상	NNG	NNG	_	0	_	_	_
8	디자이너	디자이너	NNG	NNG	_	0	_	_	_
9	엠마누엘	엠마누엘	NNP	NNP	_	0	_	_	_
10	웅가로	웅가로	NNP	NNP	_	0	_	_	_
11	가	가	JKS	JKS	_	0	_	_	_
12	실내	실내	NNG	NNG	_	0	_	_	_
13	장식	장식	NNG	NNG	_	0	_	_	_
14	용	용	XSN	XSN	_	0	_	_	_
15	직물	직물	NNG	NNG	_	0	_	_	_
16	디자이너	디자이너	NNG	NNG	_	0	_	_	_
17	로	로	JKB	JKB	_	0	_	_	_
18	나서	나서	VV	VV	_	0	_	_	_
19	었	었	EP	EP	_	0	_	_	_
20	다	다	EF	EF	_	0	_	_	_
21	.	.	SF	SF	_	0	_	_	_

$ cat sejong/tagged_input.sample | ./test_sejong.sh -v -v
Input: 프랑스 의 세계 적 이 ᆫ 의상 디자이너 엠마누엘 웅가로 가 실내 장식 용 직물 디자이너 로 나서 었 다 .
Parse:
. SF ROOT
 +-- 다 EF MOD
     +-- 었 EP MOD
         +-- 나서 VV MOD
             +-- 가 JKS NP_SBJ
             |   +-- 웅가로 NNP MOD
             |       +-- 디자이너 NNG NP
             |       |   +-- 의 JKG NP_MOD
             |       |   |   +-- 프랑스 NNP MOD
             |       |   +-- ᆫ ETM VNP_MOD
             |       |   |   +-- 이 VCP MOD
             |       |   |       +-- 적 XSN MOD
             |       |   |           +-- 세계 NNG MOD
             |       |   +-- 의상 NNG NP
             |       +-- 엠마누엘 NNP NP
             +-- 로 JKB NP_AJT
                 +-- 디자이너 NNG MOD
                     +-- 직물 NNG NP
                         +-- 실내 NNG NP
                         +-- 용 XSN NP
                             +-- 장식 NNG MOD

apply korean POS tagger(Komoran via konlpy)

# after installing konlpy ( http://konlpy.org/ko/v0.4.3/ )
$ python sejong/tagger.py
나는 학교에 간다.
1	나	나	NP	NP	_	0	_	_	_
2	는	는	JX	JX	_	0	_	_	_
3	학교	학교	NNG	NNG	_	0	_	_	_
4	에	에	JKB	JKB	_	0	_	_	_
5	가	가	VV	VV	_	0	_	_	_
6	ㄴ다	ㄴ다	EF	EF	_	0	_	_	_
7	.	.	SF	SF	_	0	_	_	_

$ echo "나는 학교에 간다." | python sejong/tagger.py | ./test_sejong.sh
Input: 나 는 학교 에 가 ㄴ다 .
Parse:
. SF ROOT
 +-- ㄴ다 EF MOD
     +-- 가 VV MOD
         +-- 는 JX NP_SBJ
         |   +-- 나 NP MOD
         +-- 에 JKB NP_AJT
             +-- 학교 NNG MOD

tensorflow serving and syntaxnet

$ bazel-bin/tensorflow_serving/example/parsey_client --server=localhost:9000
나는 학교에 간다
Input :  나는 학교에 간다
Parsing :
{"result": [{"text": "나 는 학교 에 가 ㄴ다", "token": [{"category": "NP", "head": 1, "end": 2, "label": "MOD", "start": 0, "tag": "NP", "word": "나"}, {"category": "JX", "head": 4, "end": 6, "label": "NP_SBJ", "start": 4, "tag": "JX", "word": "는"}, {"category": "NNG", "head": 3, "end": 13, "label": "MOD", "start": 8, "tag": "NNG", "word": "학교"}, {"category": "JKB", "head": 4, "end": 17, "label": "NP_AJT", "start": 15, "tag": "JKB", "word": "에"}, {"category": "VV", "head": 5, "end": 21, "label": "MOD", "start": 19, "tag": "VV", "word": "가"}, {"category": "EC", "end": 28, "label": "ROOT", "start": 23, "tag": "EC", "word": "ㄴ다"}], "docid": "-:0"}]}
...

parsey's cousins

a collection of pretrained syntactic models
how to test

# download models from http://download.tensorflow.org/models/parsey_universal/<language>.zip

# for `English`
$ echo "Bob brought the pizza to Alice." | ./parse.sh

# tokenizing
Bob brought the pizza to Alice .

# morphological analysis
1	Bob	_	_	_	Number=Sing|fPOS=PROPN++NNP	0	_	_	_
2	brought	_	_	_	Mood=Ind|Tense=Past|VerbForm=Fin|fPOS=VERB++VBD	0	_	_	_
3	the	_	_	_	Definite=Def|PronType=Art|fPOS=DET++DT	0	_	_	_
4	pizza	_	_	_	Number=Sing|fPOS=NOUN++NN	0	_	_	_
5	to	_	_	_	fPOS=ADP++IN	0	_	_	_
6	Alice	_	_	_	Number=Sing|fPOS=PROPN++NNP	0	_	_	_
7	.	_	_	_	fPOS=PUNCT++.	0	_	_	_

# tagging
1	Bob	_	PROPN	NNP	Number=Sing|fPOS=PROPN++NNP	0	_	_	_
2	brought	_	VERB	VBD	Mood=Ind|Tense=Past|VerbForm=Fin|fPOS=VERB++VBD	0	_	_	_
3	the	_	DET	DT	Definite=Def|PronType=Art|fPOS=DET++DT	0	_	_	_
4	pizza	_	NOUN	NN	Number=Sing|fPOS=NOUN++NN	0	_	_	_
5	to	_	ADP	IN	fPOS=ADP++IN	0	_	_	_
6	Alice	_	PROPN	NNP	Number=Sing|fPOS=PROPN++NNP	0	_	_	_
7	.	_	PUNCT	.	fPOS=PUNCT++.	0	_	_	_

# parsing
1	Bob	_	PROPN	NNP	Number=Sing|fPOS=PROPN++NNP	2	nsubj	_	_
2	brought	_	VERB	VBD	Mood=Ind|Tense=Past|VerbForm=Fin|fPOS=VERB++VBD	0	ROOT	_	_
3	the	_	DET	DT	Definite=Def|PronType=Art|fPOS=DET++DT	4	det	_	_
4	pizza	_	NOUN	NN	Number=Sing|fPOS=NOUN++NN	2	dobj	_	_
5	to	_	ADP	IN	fPOS=ADP++IN	6	case	_	_
6	Alice	_	PROPN	NNP	Number=Sing|fPOS=PROPN++NNP	2	nmod	_	_
7	.	_	PUNCT	.	fPOS=PUNCT++.	2	punct	_	_

# conll2tree 
Input: Bob brought the pizza to Alice .
Parse:
brought VERB++VBD ROOT
 +-- Bob PROPN++NNP nsubj
 +-- pizza NOUN++NN dobj
 |   +-- the DET++DT det
 +-- Alice PROPN++NNP nmod
 |   +-- to ADP++IN case
 +-- . PUNCT++. punct

downloaded model vs trained model

1. downloaded model
Language	No. tokens	POS	fPOS	Morph	UAS	LAS
-------------------------------------------------------
English	25096	90.48%	89.71%	91.30%	84.79%	80.38%

2. trained model
INFO:tensorflow:Total processed documents: 2077
INFO:tensorflow:num correct tokens: 18634
INFO:tensorflow:total tokens: 22395
INFO:tensorflow:Seconds elapsed in evaluation: 19.85, eval metric: 83.21%

3. where does the difference(84.79% - 83.21%) come from?
as mentioned https://research.googleblog.com/2016/08/meet-parseys-cousins-syntax-for-40.html
they found good hyperparameters by using MapReduce.
for example, 
the hyperparameters for POS tagger :
  - POS_PARAMS=128-0.08-3600-0.9-0
  - decay_steps=3600
  - hidden_layer_sizes=128
  - learning_rate=0.08
  - momentum=0.9

dragnn

how to compile examples

$ cd ../
$ pwd
/path/to/models/syntaxnet
$ bazel build -c opt //examples/dragnn:tutorial_1

training tagger and parser with CoNLL corpus

# compile
$ pwd
/path/to/models/syntaxnet
$ bazel build -c opt //work/dragnn_examples:write_master_spec
$ bazel build -c opt //work/dragnn_examples:train_dragnn
$ bazel build -c opt //work/dragnn_examples:inference_dragnn
# training
$ cd work
$ ./train_dragnn.sh -v -v
...
INFO:tensorflow:training step: 25300, actual: 25300
INFO:tensorflow:training step: 25400, actual: 25400
INFO:tensorflow:finished step: 25400, actual: 25400
INFO:tensorflow:Annotating datset: 2002 examples
INFO:tensorflow:Done. Produced 2002 annotations
INFO:tensorflow:Total num documents: 2002
INFO:tensorflow:Total num tokens: 25148
INFO:tensorflow:POS: 85.63%
INFO:tensorflow:UAS: 79.67%
INFO:tensorflow:LAS: 74.36%
...
# test
$ echo "i love this one" | ./test_dragnn.sh
Input: i love this one
Parse:
love VBP root
 +-- i PRP nsubj
 +-- one CD obj
     +-- this DT det

training parser with Sejong corpus

# compile
$ pwd
/path/to/models/syntaxnet
$ bazel build -c opt //work/dragnn_examples:write_master_spec
$ bazel build -c opt //work/dragnn_examples:train_dragnn
$ bazel build -c opt //work/dragnn_examples:inference_dragnn_sejong
# training
$ cd work
# to prepare corpus, please refer to `training parser from Sejong treebank corpus` section.
$ ./train_dragnn_sejong.sh -v -v
...
INFO:tensorflow:training step: 33100, actual: 33100
INFO:tensorflow:training step: 33200, actual: 33200
INFO:tensorflow:finished step: 33200, actual: 33200
INFO:tensorflow:Annotating datset: 4114 examples
INFO:tensorflow:Done. Produced 4114 annotations
INFO:tensorflow:Total num documents: 4114
INFO:tensorflow:Total num tokens: 97002
INFO:tensorflow:POS: 93.95%
INFO:tensorflow:UAS: 91.38%
INFO:tensorflow:LAS: 87.76%
...
# test
# after installing konlpy ( http://konlpy.org/ko/v0.4.3/ )
$ echo "제주로 가는 비행기가 심한 비바람에 회항했다." | ./test_dragnn_sejong.sh
INFO:tensorflow:Read 1 documents
Input: 제주 로 가 는 비행기 가 심하 ㄴ 비바람 에 회항 하 았 다 .
Parse:
. SF VP
 +-- 다 EF MOD
     +-- 았 EP MOD
         +-- 하 XSA MOD
             +-- 회항 SN MOD
                 +-- 가 JKS NP_SBJ
                 |   +-- 비행기 NNG MOD
                 |       +-- 는 ETM VP_MOD
                 |           +-- 가 VV MOD
                 |               +-- 로 JKB NP_AJT
                 |                   +-- 제주 MAG MOD
                 +-- 에 JKB NP_AJT
                     +-- 비바람 NNG MOD
                         +-- ㄴ SN MOD
                             +-- 심하 VV NP
# it seems that pos tagging results from the dragnn are somewhat incorrect.
# so, i replace those to the results from the Komoran tagger.
# you can modify 'inference_dragnn_sejong.py' to use the tags from the dragnn.
Input: 제주 로 가 는 비행기 가 심하 ㄴ 비바람 에 회항 하 았 다 .
Parse:
. SF VP
 +-- 다 EF MOD
     +-- 았 EP MOD
         +-- 하 XSV MOD
             +-- 회항 NNG MOD
                 +-- 가 JKS NP_SBJ
                 |   +-- 비행기 NNG MOD
                 |       +-- 는 ETM VP_MOD
                 |           +-- 가 VV MOD
                 |               +-- 로 JKB NP_AJT
                 |                   +-- 제주 NNG MOD
                 +-- 에 JKB NP_AJT
                     +-- 비바람 NNG MOD
                         +-- ㄴ ETM MOD
                             +-- 심하 VA NP

web api using tornado

how to run

# compile
$ pwd
/path/to/models/syntaxnet
$ bazel build -c opt //work/dragnn_examples:dragnn_dm
# start tornado web api
$ cd work/dragnn_examples/www
# start single process
$ ./start.sh -v -v 0 0
# despite tornado suppoting multi-processing, session of tensorflow is not fork-safe.
# so do not use multi-processing option.
# if you want to link to the model trained by Sejong corpus, just edit env.sh
# : enable_konlpy='True'

# http://hostip:8897 
# http://hostip:8897/dragnn?q=i love it
# http://hostip:8897/dragnn?q=나는 학교에 가서 공부했다.

api output format(sample)

brat annotation tool

comparison to BIST parser

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