99 lines
8.4 KiB
Plaintext
99 lines
8.4 KiB
Plaintext
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Pretrained Punkt Models -- Jan Strunk (New version trained after issues 313 and 514 had been corrected)
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Most models were prepared using the test corpora from Kiss and Strunk (2006). Additional models have
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been contributed by various people using NLTK for sentence boundary detection.
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For information about how to use these models, please confer the tokenization HOWTO:
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http://nltk.googlecode.com/svn/trunk/doc/howto/tokenize.html
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and chapter 3.8 of the NLTK book:
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http://nltk.googlecode.com/svn/trunk/doc/book/ch03.html#sec-segmentation
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There are pretrained tokenizers for the following languages:
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File Language Source Contents Size of training corpus(in tokens) Model contributed by
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=======================================================================================================================================================================
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czech.pickle Czech Multilingual Corpus 1 (ECI) Lidove Noviny ~345,000 Jan Strunk / Tibor Kiss
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Literarni Noviny
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danish.pickle Danish Avisdata CD-Rom Ver. 1.1. 1995 Berlingske Tidende ~550,000 Jan Strunk / Tibor Kiss
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(Berlingske Avisdata, Copenhagen) Weekend Avisen
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dutch.pickle Dutch Multilingual Corpus 1 (ECI) De Limburger ~340,000 Jan Strunk / Tibor Kiss
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english.pickle English Penn Treebank (LDC) Wall Street Journal ~469,000 Jan Strunk / Tibor Kiss
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(American)
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estonian.pickle Estonian University of Tartu, Estonia Eesti Ekspress ~359,000 Jan Strunk / Tibor Kiss
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finnish.pickle Finnish Finnish Parole Corpus, Finnish Books and major national ~364,000 Jan Strunk / Tibor Kiss
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Text Bank (Suomen Kielen newspapers
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Tekstipankki)
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Finnish Center for IT Science
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(CSC)
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french.pickle French Multilingual Corpus 1 (ECI) Le Monde ~370,000 Jan Strunk / Tibor Kiss
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(European)
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german.pickle German Neue Zürcher Zeitung AG Neue Zürcher Zeitung ~847,000 Jan Strunk / Tibor Kiss
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(Switzerland) CD-ROM
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(Uses "ss"
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instead of "ß")
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greek.pickle Greek Efstathios Stamatatos To Vima (TO BHMA) ~227,000 Jan Strunk / Tibor Kiss
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italian.pickle Italian Multilingual Corpus 1 (ECI) La Stampa, Il Mattino ~312,000 Jan Strunk / Tibor Kiss
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norwegian.pickle Norwegian Centre for Humanities Bergens Tidende ~479,000 Jan Strunk / Tibor Kiss
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(Bokmål and Information Technologies,
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Nynorsk) Bergen
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polish.pickle Polish Polish National Corpus Literature, newspapers, etc. ~1,000,000 Krzysztof Langner
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(http://www.nkjp.pl/)
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portuguese.pickle Portuguese CETENFolha Corpus Folha de São Paulo ~321,000 Jan Strunk / Tibor Kiss
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(Brazilian) (Linguateca)
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slovene.pickle Slovene TRACTOR Delo ~354,000 Jan Strunk / Tibor Kiss
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Slovene Academy for Arts
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and Sciences
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spanish.pickle Spanish Multilingual Corpus 1 (ECI) Sur ~353,000 Jan Strunk / Tibor Kiss
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(European)
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swedish.pickle Swedish Multilingual Corpus 1 (ECI) Dagens Nyheter ~339,000 Jan Strunk / Tibor Kiss
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(and some other texts)
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turkish.pickle Turkish METU Turkish Corpus Milliyet ~333,000 Jan Strunk / Tibor Kiss
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(Türkçe Derlem Projesi)
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University of Ankara
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The corpora contained about 400,000 tokens on average and mostly consisted of newspaper text converted to
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Unicode using the codecs module.
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Kiss, Tibor and Strunk, Jan (2006): Unsupervised Multilingual Sentence Boundary Detection.
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Computational Linguistics 32: 485-525.
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---- Training Code ----
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# import punkt
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import nltk.tokenize.punkt
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# Make a new Tokenizer
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tokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()
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# Read in training corpus (one example: Slovene)
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import codecs
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text = codecs.open("slovene.plain","Ur","iso-8859-2").read()
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# Train tokenizer
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tokenizer.train(text)
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# Dump pickled tokenizer
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import pickle
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out = open("slovene.pickle","wb")
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pickle.dump(tokenizer, out)
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out.close()
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---------
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