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