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

• Web and HomePage

• PyPi Python Package

Install

In Terminal

>>>bash$ python setup.py install

History

• 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

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

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

In Python

>>>from jNlp.jCabocha import JCabocha
>>>cabocha = JCabocha()
>>>print cabocha.xmlstring(input_sentence).encode('utf-8')

Output:

<sentence>
 <chunk id="0" link="8" rel="D" score="0.971639" head="0" func="1">
  <tok id="0" read="ワタシ" base="私" pos="名詞-代名詞-一般" ctype="" cform="" ne="O">私</tok>
  <tok id="1" read="ハ" base="は" pos="助詞-係助詞" ctype="" cform="" ne="O">は</tok>
 </chunk>
 <chunk id="1" link="2" rel="D" score="0.488672" head="2" func="3">
  <tok id="2" read="カレ" base="彼" pos="名詞-代名詞-一般" ctype="" cform="" ne="O">彼</tok>
  <tok id="3" read="ヲ" base="を" pos="助詞-格助詞-一般" ctype="" cform="" ne="O">を</tok>
 </chunk>
 <chunk id="2" link="8" rel="D" score="2.25834" head="6" func="6">
  <tok id="4" read="ゴ" base="５" pos="名詞-数" ctype="" cform="" ne="B-DATE">５</tok>
  <tok id="5" read="ニチ" base="日" pos="名詞-接尾-助数詞" ctype="" cform="" ne="I-DATE">日</tok>
  <tok id="6" read="マエ" base="前" pos="名詞-副詞可能" ctype="" cform="" ne="I-DATE">前</tok>
  <tok id="7" read="、" base="、" pos="記号-読点" ctype="" cform="" ne="O">、</tok>
 </chunk>

Similarily

>>>print cabocha.lattice(input_sentence).encode('utf-8') #will print lattice
>>>print cabocha.corpus(input_sentence).encode('utf-8') #will print corpus view

Kanji / Katakana /Hiragana to Tokenized Romaji jConvert.py

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

Usage

>>>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)

Output

...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

• katakanaChartFile and hiraganaChartFile

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 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 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

edict_search.py

author:	Paul Goins License included linkToOriginal:

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.
  ...