"""
-f = open('index.html','a')
-f.write(fork_me)
-f.close()
diff --git a/jnlp/index.html b/jnlp/index.html
deleted file mode 100644
index 3290476..0000000
--- a/jnlp/index.html
+++ /dev/null
@@ -1,707 +0,0 @@
-
-
-
-
-
-
-Contents
- --clone git@github.com:kevincobain2000/jProcessing.git --
0.2
----
-- Sentiment Analysis of Japanese Text
-
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
-
-Run Cabocha with original EUCJP or IS0-8859-1 configured encoding, with utf8 python
-In Python
-
->>>from jNlp.jCabocha import cabocha
->>>print cabocha(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> --
Uses data/katakanaChart.txt and parses the chart. See katakanaChart.
-Usage
-->>>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) --
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
- -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 --
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
-
-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.
-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.
-| 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')
-
-| 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. - ... --
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).
- -The following classifier is baseline, which works as simple mapping of Eng to Japanese using Wordnet and classify on polarity score using SentiWordnet.
-->>> 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 --
->>> 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
-
-- ---
-- - - - - Author: pulkit[at]jaist.ac.jp [change at with @] -
\ No newline at end of file
diff --git a/jnlp/index.rst b/jnlp/index.rst
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--- a/jnlp/index.rst
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@@ -1,359 +0,0 @@
-====================
-Japanese NLP Library
-====================
-
-
-.. sectnum::
-.. contents::
-
-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
-
-In ``Python`` ::
-
->>>from jNlp.jCabocha import cabocha
->>>print cabocha(input_sentence).encode('utf-8')
-
-Output:
-
-::
-
-