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====================
Japanese NLP Library

.. sectnum::
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Requirements

• Third Party Dependencies

  • Cabocha Japanese Morphological parser http://sourceforge.net/projects/cabocha/

• Python Dependencies

  • Python 2.6.* or above

Links

• All code at jProcessing Repo GitHub_

.. _GitHub: https://github.com/kevincobain2000/jProcessing

• Documentation_ and HomePage_ and Sphinx_

.. _Documentation: http://www.jaist.ac.jp/~s1010205/jnlp

.. _HomePage: http://www.jaist.ac.jp/~s1010205/

.. _Sphinx: http://readthedocs.org/docs/jprocessing/en/latest/

• PyPi_ Python Package

.. _PyPi: http://pypi.python.org/pypi/jProcessing/0.1

::

clone git@github.com:kevincobain2000/jProcessing.git

Install

In Terminal ::

bash$ python setup.py install

History

• 0.2

    + Sentiment Analysis of Japanese Text
  

• 0.1
  + Morphologically Tokenize Japanese Sentence
  + Kanji / Hiragana / Katakana to Romaji Converter
  + Edict Dictionary Search - borrowed
  + Edict Examples Search - incomplete
  + Sentence Similarity between two JP Sentences
  + Run Cabocha(ISO--8859-1 configured) in Python.
  + Longest Common String between Sentences
  + Kanji to Katakana Pronunciation
  + Hiragana, Katakana Chart Parser

Libraries and Modules

Tokenize jTokenize.py

In Python ::

from jNlp.jTokenize import jTokenize
input_sentence = u'私は彼を５日前、つまりこの前の金曜日に駅で見かけた'
list_of_tokens = jTokenize(input_sentence)
print list_of_tokens
print '--'.join(list_of_tokens).encode('utf-8')

Returns:

::

... [u'\u79c1', u'\u306f', u'\u5f7c', u'\u3092', u'\uff15'...]
... 私--は--彼--を--５--日--前--、--つまり--この--前--の--金曜日--に--駅--で--見かけ--た

Katakana Pronunciation:

::

print '--'.join(jReads(input_sentence)).encode('utf-8')
... ワタシ--ハ--カレ--ヲ--ゴ--ニチ--マエ--、--ツマリ--コノ--マエ--ノ--キンヨウビ--ニ--エキ--デ--ミカケ--タ

Cabocha jCabocha.py

Run Cabocha_ with original EUCJP or IS0-8859-1 configured encoding, with utf8 python

.. _Cabocha: http://code.google.com/p/cabocha/

• If cobocha is configured as utf8 then see this http://nltk.googlecode.com/svn/trunk/doc/book-jp/ch12.html#cabocha

.. code-block:: python

from jNlp.jCabocha import cabocha
print cabocha(input_sentence).encode('utf-8')

Output:

.. code-block:: xml

Kanji / Katakana /Hiragana to Tokenized Romaji jConvert.py

Uses data/katakanaChart.txt and parses the chart. See katakanaChart_.

.. code-block:: python

from jNlp.jConvert import *
input_sentence = u'気象庁が２１日午前４時４８分、発表した天気概況によると、'
print ' '.join(tokenizedRomaji(input_sentence))
print tokenizedRomaji(input_sentence)

.. code-block:: python

...kisyoutyou ga ni ichi nichi gozen yon ji yon hachi hun hapyou si ta tenki gaikyou ni yoru to
...[u'kisyoutyou', u'ga', u'ni', u'ichi', u'nichi', u'gozen',...]

katakanaChart.txt

.. _katakanaChart:

• katakanaChartFile_ and hiraganaChartFile_

.. _katakanaChartFile: https://raw.github.com/kevincobain2000/jProcessing/master/src/jNlp/data/katakanaChart.txt

.. _hiraganaChartFile: https://raw.github.com/kevincobain2000/jProcessing/master/src/jNlp/data/hiraganaChart.txt

Longest Common String Japanese jProcessing.py

On English Strings ::

from jNlp.jProcessing import long_substr
a = 'Once upon a time in Italy'
b = 'Thre was a time in America'
print long_substr(a, b)

Output ::

...a time in

On Japanese Strings ::

a = u'これでアナタも冷え知らず'
b = u'これでア冷え知らずナタも'
print long_substr(a, b).encode('utf-8')

Output ::

...冷え知らず

Similarity between two sentences jProcessing.py

Uses MinHash by checking the overlap http://en.wikipedia.org/wiki/MinHash

:English Strings:

from jNlp.jProcessing import Similarities
s = Similarities()
a = 'There was'
b = 'There is'
print s.minhash(a,b)
...0.444444444444

:Japanese Strings:

from jNlp.jProcessing import *
a = u'これは何ですか？'
b = u'これはわからないです'
print s.minhash(' '.join(jTokenize(a)), ' '.join(jTokenize(b)))
...0.210526315789

Edict Japanese Dictionary Search with Example sentences

Sample Ouput Demo

.. raw:: html

Edict dictionary and example sentences parser.

This package uses the EDICT_ and KANJIDIC_ dictionary files.
These files are the property of the
Electronic Dictionary Research and Development Group_ , and
are used in conformance with the Group's licence_ .

.. _EDICT: http://www.csse.monash.edu.au/~jwb/edict.html
.. _KANJIDIC: http://www.csse.monash.edu.au/~jwb/kanjidic.html
.. _Group: http://www.edrdg.org/
.. _licence: http://www.edrdg.org/edrdg/licence.html

Edict Parser By Paul Goins, see edict_search.py
Edict Example sentences Parse by query, Pulkit Kathuria, see edict_examples.py
Edict examples pickle files are provided but latest example files can be downloaded from the links provided.

Charset

Two files

• utf8 Charset example file if not using src/jNlp/data/edict_examples

  To convert EUCJP/ISO-8859-1 to utf8 ::

  iconv -f EUCJP -t UTF-8 path/to/edict_examples > path/to/save_with_utf-8

• ISO-8859-1 edict_dictionary file

Outputs example sentences for a query in Japanese only for ambiguous words.

Links

Latest Dictionary files can be downloaded here_

.. _here: http://www.csse.monash.edu.au/~jwb/edict.html

edict_search.py

:author: Paul Goins License included linkToOriginal_:

.. _linkToOriginal: http://repo.or.cz/w/jbparse.git/blame/8e42831ca5f721c0320b27d7d83cb553d6e9c68f:/jbparse/edict.py

For all entries of sense definitions

from jNlp.edict_search import *
query = u'認める'
edict_path = 'src/jNlp/data/edict-yy-mm-dd'
kp = Parser(edict_path)
for i, entry in enumerate(kp.search(query)):
... print entry.to_string().encode('utf-8')

edict_examples.py

:Note: Only outputs the examples sentences for ambiguous words (if word has one or more senses)

:author: Pulkit Kathuria

from jNlp.edict_examples import *
query = u'認める'
edict_path = 'src/jNlp/data/edict-yy-mm-dd'
edict_examples_path = 'src/jNlp/data/edict_examples'
search_with_example(edict_path, edict_examples_path, query)

Output ::

認める

Sense (1) to recognize;
EX:01 我々は彼の才能を認めている。We appreciate his talent.

Sense (2) to observe;
EX:01 ｘ線写真で異状が認められます。We have detected an abnormality on your x-ray.

Sense (3) to admit;
EX:01 母は私の計画をよいと認めた。Mother approved my plan.
EX:02 母は決して私の結婚を認めないだろう。Mother will never approve of my marriage.
EX:03 父は決して私の結婚を認めないだろう。Father will never approve of my marriage.
EX:04 彼は女性の喫煙をいいものだと認めない。He doesn't approve of women smoking.
...

Sentiment Analysis Japanese Text

This section covers (1) Sentiment Analysis on Japanese text using Word Sense Disambiguation, Wordnet-jp_ (Japanese Word Net file name wnjpn-all.tab), SentiWordnet_ (English SentiWordNet file name SentiWordNet_3.*.txt).

.. _Wordnet-jp: http://nlpwww.nict.go.jp/wn-ja/eng/downloads.html
.. _SentiWordnet: http://sentiwordnet.isti.cnr.it/

Wordnet files download links

1. http://nlpwww.nict.go.jp/wn-ja/eng/downloads.html
2. http://sentiwordnet.isti.cnr.it/

How to Use

The following classifier is baseline, which works as simple mapping of Eng to Japanese using Wordnet and classify on polarity score using SentiWordnet.

• (Adnouns, nouns, verbs, .. all included)
• No WSD module on Japanese Sentence
• Uses word as its common sense for polarity score

from jNlp.jSentiments import *
jp_wn = '../../../../data/wnjpn-all.tab'
en_swn = '../../../../data/SentiWordNet_3.0.0_20100908.txt'
classifier = Sentiment()
classifier.train(en_swn, jp_wn)
text = u'監督、俳優、ストーリー、演出、全部最高！'
print classifier.baseline(text)
...Pos Score = 0.625 Neg Score = 0.125
...Text is Positive

Japanese Word Polarity Score

from jNlp.jSentiments import *
jp_wn = '_dicts/wnjpn-all.tab' #path to Japanese Word Net
en_swn = '_dicts/SentiWordNet_3.0.0_20100908.txt' #Path to SentiWordNet
classifier = Sentiment()
sentiwordnet, jpwordnet = classifier.train(en_swn, jp_wn)
positive_score = sentiwordnet[jpwordnet[u'全部']][0]
negative_score = sentiwordnet[jpwordnet[u'全部']][1]
print 'pos score = {0}, neg score = {1}'.format(positive_score, negative_score)
...pos score = 0.625, neg score = 0.0

Contacts

:Author: pulkit[at]jaist.ac.jp [change at with @]

.. include:: disqus_jnlp.html.rst