A Python Wrapper for Google SyntaxNet

Installation

Prerequisites

Install OpenJDK8.

add-apt-repository -y ppa:openjdk-r/ppa
apt-get -y update
apt-get -y install openjdk-8-jdk

Install bazel and include bazel in $PATH.

Note: Only bazel 0.4.3 is runnable. bazel 0.4.4 may cause errors.

wget https://github.com/bazelbuild/bazel/releases/download/0.4.3/bazel-0.4.3-installer-linux-x86_64.sh
chmod +x bazel-0.4.3-installer-linux-x86_64.sh
./bazel-0.4.3-installer-linux-x86_64.sh --user
rm bazel-0.4.3-installer-linux-x86_64.shexport PATH="$PATH:$HOME/bin"

Install system package dependencies.

apt-get -y install swig unzip

Install Python packages

Note: Current version of syntaxnet must be used with tensorflow r1.0.

pip install tensorflow protobuf asciitree mock

Start Installing

pip install git+ssh://git@github.com/livingbio/syntaxnet_wrapper.git#egg=syntaxnet_wrapper

If installation failed...

Execute test.sh, you should see following outputs:

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    _       _

1       球      _       PROPN   NNP     fPOS=PROPN++NNP 4       nsubj   _       _
2       從      _       ADP     IN      fPOS=ADP++IN    3       case    _       _
3       天上    _       NOUN    NN      fPOS=NOUN++NN   4       nmod    _       _
4       掉      _       VERB    VV      fPOS=VERB++VV   0       ROOT    _       _
5       下來    _       VERB    VV      fPOS=VERB++VV   4       mark    _       _

球 從天 上 掉 下 來

If the outputs are correct, problems are caused by the wrapper. If the outputs are wrong, compilation of syntaxnet may be failed.

Usage

from syntaxnet_wrapper import tagger, parserprint tagger['en'].query('this is a good day', returnRaw=True)# 1       this    _       DET     DT      _       0       _       _       _# 2       is      _       VERB    VBZ     _       0       _       _       _# 3       a       _       DET     DT      _       0       _       _       _# 4       good    _       ADJ     JJ      _       0       _       _       _# 5       day     _       NOUN    NN      _       0       _       _       _tagger['en'].query('this is a good day')  # in default, return splitted textprint parser['en'].query('Alice drove down the street in her car', returnRaw=True)# 1       Alice   _       NOUN    NNP     _       2       nsubj   _       _# 2       drove   _       VERB    VBD     _       0       ROOT    _       _# 3       down    _       ADP     IN      _       2       prep    _       _# 4       the     _       DET     DT      _       5       det     _       _# 5       street  _       NOUN    NN      _       3       pobj    _       _# 6       in      _       ADP     IN      _       2       prep    _       _# 7       her     _       PRON    PRP$    _       8       poss    _       _# 8       car     _       NOUN    NN      _       6       pobj    _       _# use Chinese modelprint tagger['zh'].query(u'今天 天氣 很 好', returnRaw=True)# 1       今天    _       NOUN    NN      fPOS=NOUN++NN   0       _       _       _# 2       天氣    _       NOUN    NN      fPOS=NOUN++NN   0       _       _       _# 3       很      _       ADV     RB      fPOS=ADV++RB    0       _       _       _# 4       好      _       ADJ     JJ      fPOS=ADJ++JJ    0       _       _       _print parser['zh'].query(u'今天 天氣 很 好', returnRaw=True)# 1       今天    _       NOUN    NN      fPOS=NOUN++NN   4       nmod:tmod       _       _# 2       天氣    _       NOUN    NN      fPOS=NOUN++NN   4       nsubj   _       _# 3       很      _       ADV     RB      fPOS=ADV++RB    4       advmod  _       _# 4       好      _       ADJ     JJ      fPOS=ADJ++JJ    0       ROOT    _       _

Language Selection

The default model is 'English-Parsey'. This isannounced by Googleon May, 2016. Other models, includes 'English', are trained by Universal Dependencies,announced by Googleon August, 2016.

from syntaxnet_wrapper import language_code_to_model_name
language_code_to_model_name# {'ar': 'Arabic',#  'bg': 'Bulgarian',#  'ca': 'Catalan',#  'cs': 'Czech',#  'da': 'Danish',#  'de': 'German',#  'el': 'Greek',#  'en': 'English-Parsey',#  'en-uni': 'English',#  'es': 'Spanish',#  'et': 'Estonian',#  'eu': 'Basque',#  'fa': 'Persian',#  'fi': 'Finnish',#  'fr': 'French',#  'ga': 'Irish',#  'gl': 'Galician',#  'hi': 'Hindi',#  'hr': 'Croatian',#  'hu': 'Hungarian',#  'id': 'Indonesian',#  'it': 'Italian',#  'iw': 'Hebrew',#  'kk': 'Kazakh',#  'la': 'Latin',#  'lv': 'Latvian',#  'nl': 'Dutch',#  'no': 'Norwegian',#  'pl': 'Polish',#  'pt': 'Portuguese',#  'ro': 'Romanian',#  'ru': 'Russian',#  'sl': 'Slovenian',#  'sv': 'Swedish',#  'ta': 'Tamil',#  'tr': 'Turkish',#  'zh': 'Chinese',#  'zh-cn': 'Chinese',#  'zh-tw': 'Chinese'}