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  1. nltk_data/tokenizers/punkt/PY3/README +0 -98
  2. nltk_data/tokenizers/punkt/PY3/czech.pickle +0 -3
  3. nltk_data/tokenizers/punkt/PY3/danish.pickle +0 -3
  4. nltk_data/tokenizers/punkt/PY3/dutch.pickle +0 -3
  5. nltk_data/tokenizers/punkt/PY3/english.pickle +0 -3
  6. nltk_data/tokenizers/punkt/PY3/estonian.pickle +0 -3
  7. nltk_data/tokenizers/punkt/PY3/finnish.pickle +0 -3
  8. nltk_data/tokenizers/punkt/PY3/french.pickle +0 -3
  9. nltk_data/tokenizers/punkt/PY3/german.pickle +0 -3
  10. nltk_data/tokenizers/punkt/PY3/greek.pickle +0 -3
  11. nltk_data/tokenizers/punkt/PY3/italian.pickle +0 -3
  12. nltk_data/tokenizers/punkt/PY3/malayalam.pickle +0 -3
  13. nltk_data/tokenizers/punkt/PY3/norwegian.pickle +0 -3
  14. nltk_data/tokenizers/punkt/PY3/polish.pickle +0 -3
  15. nltk_data/tokenizers/punkt/PY3/portuguese.pickle +0 -3
  16. nltk_data/tokenizers/punkt/PY3/russian.pickle +0 -3
  17. nltk_data/tokenizers/punkt/PY3/slovene.pickle +0 -3
  18. nltk_data/tokenizers/punkt/PY3/spanish.pickle +0 -3
  19. nltk_data/tokenizers/punkt/PY3/swedish.pickle +0 -3
  20. nltk_data/tokenizers/punkt/PY3/turkish.pickle +0 -3
  21. nltk_data/tokenizers/punkt/README +0 -98
  22. nltk_data/tokenizers/punkt/czech.pickle +0 -3
  23. nltk_data/tokenizers/punkt/danish.pickle +0 -3
  24. nltk_data/tokenizers/punkt/dutch.pickle +0 -3
  25. nltk_data/tokenizers/punkt/english.pickle +0 -3
  26. nltk_data/tokenizers/punkt/estonian.pickle +0 -3
  27. nltk_data/tokenizers/punkt/finnish.pickle +0 -3
  28. nltk_data/tokenizers/punkt/french.pickle +0 -3
  29. nltk_data/tokenizers/punkt/german.pickle +0 -3
  30. nltk_data/tokenizers/punkt/greek.pickle +0 -3
  31. nltk_data/tokenizers/punkt/italian.pickle +0 -3
  32. nltk_data/tokenizers/punkt/malayalam.pickle +0 -3
  33. nltk_data/tokenizers/punkt/norwegian.pickle +0 -3
  34. nltk_data/tokenizers/punkt/polish.pickle +0 -3
  35. nltk_data/tokenizers/punkt/portuguese.pickle +0 -3
  36. nltk_data/tokenizers/punkt/russian.pickle +0 -3
  37. nltk_data/tokenizers/punkt/slovene.pickle +0 -3
  38. nltk_data/tokenizers/punkt/spanish.pickle +0 -3
  39. nltk_data/tokenizers/punkt/swedish.pickle +0 -3
  40. nltk_data/tokenizers/punkt/turkish.pickle +0 -3
nltk_data/tokenizers/punkt/PY3/README DELETED
@@ -1,98 +0,0 @@
1
- Pretrained Punkt Models -- Jan Strunk (New version trained after issues 313 and 514 had been corrected)
2
-
3
- Most models were prepared using the test corpora from Kiss and Strunk (2006). Additional models have
4
- been contributed by various people using NLTK for sentence boundary detection.
5
-
6
- For information about how to use these models, please confer the tokenization HOWTO:
7
- http://nltk.googlecode.com/svn/trunk/doc/howto/tokenize.html
8
- and chapter 3.8 of the NLTK book:
9
- http://nltk.googlecode.com/svn/trunk/doc/book/ch03.html#sec-segmentation
10
-
11
- There are pretrained tokenizers for the following languages:
12
-
13
- File Language Source Contents Size of training corpus(in tokens) Model contributed by
14
- =======================================================================================================================================================================
15
- czech.pickle Czech Multilingual Corpus 1 (ECI) Lidove Noviny ~345,000 Jan Strunk / Tibor Kiss
16
- Literarni Noviny
17
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
18
- danish.pickle Danish Avisdata CD-Rom Ver. 1.1. 1995 Berlingske Tidende ~550,000 Jan Strunk / Tibor Kiss
19
- (Berlingske Avisdata, Copenhagen) Weekend Avisen
20
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
21
- dutch.pickle Dutch Multilingual Corpus 1 (ECI) De Limburger ~340,000 Jan Strunk / Tibor Kiss
22
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
23
- english.pickle English Penn Treebank (LDC) Wall Street Journal ~469,000 Jan Strunk / Tibor Kiss
24
- (American)
25
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
26
- estonian.pickle Estonian University of Tartu, Estonia Eesti Ekspress ~359,000 Jan Strunk / Tibor Kiss
27
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
28
- finnish.pickle Finnish Finnish Parole Corpus, Finnish Books and major national ~364,000 Jan Strunk / Tibor Kiss
29
- Text Bank (Suomen Kielen newspapers
30
- Tekstipankki)
31
- Finnish Center for IT Science
32
- (CSC)
33
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
34
- french.pickle French Multilingual Corpus 1 (ECI) Le Monde ~370,000 Jan Strunk / Tibor Kiss
35
- (European)
36
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
37
- german.pickle German Neue Zürcher Zeitung AG Neue Zürcher Zeitung ~847,000 Jan Strunk / Tibor Kiss
38
- (Switzerland) CD-ROM
39
- (Uses "ss"
40
- instead of "ß")
41
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
42
- greek.pickle Greek Efstathios Stamatatos To Vima (TO BHMA) ~227,000 Jan Strunk / Tibor Kiss
43
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
44
- italian.pickle Italian Multilingual Corpus 1 (ECI) La Stampa, Il Mattino ~312,000 Jan Strunk / Tibor Kiss
45
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
46
- norwegian.pickle Norwegian Centre for Humanities Bergens Tidende ~479,000 Jan Strunk / Tibor Kiss
47
- (Bokmål and Information Technologies,
48
- Nynorsk) Bergen
49
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
50
- polish.pickle Polish Polish National Corpus Literature, newspapers, etc. ~1,000,000 Krzysztof Langner
51
- (http://www.nkjp.pl/)
52
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
53
- portuguese.pickle Portuguese CETENFolha Corpus Folha de São Paulo ~321,000 Jan Strunk / Tibor Kiss
54
- (Brazilian) (Linguateca)
55
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
56
- slovene.pickle Slovene TRACTOR Delo ~354,000 Jan Strunk / Tibor Kiss
57
- Slovene Academy for Arts
58
- and Sciences
59
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
60
- spanish.pickle Spanish Multilingual Corpus 1 (ECI) Sur ~353,000 Jan Strunk / Tibor Kiss
61
- (European)
62
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
63
- swedish.pickle Swedish Multilingual Corpus 1 (ECI) Dagens Nyheter ~339,000 Jan Strunk / Tibor Kiss
64
- (and some other texts)
65
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
66
- turkish.pickle Turkish METU Turkish Corpus Milliyet ~333,000 Jan Strunk / Tibor Kiss
67
- (Türkçe Derlem Projesi)
68
- University of Ankara
69
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
70
-
71
- The corpora contained about 400,000 tokens on average and mostly consisted of newspaper text converted to
72
- Unicode using the codecs module.
73
-
74
- Kiss, Tibor and Strunk, Jan (2006): Unsupervised Multilingual Sentence Boundary Detection.
75
- Computational Linguistics 32: 485-525.
76
-
77
- ---- Training Code ----
78
-
79
- # import punkt
80
- import nltk.tokenize.punkt
81
-
82
- # Make a new Tokenizer
83
- tokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()
84
-
85
- # Read in training corpus (one example: Slovene)
86
- import codecs
87
- text = codecs.open("slovene.plain","Ur","iso-8859-2").read()
88
-
89
- # Train tokenizer
90
- tokenizer.train(text)
91
-
92
- # Dump pickled tokenizer
93
- import pickle
94
- out = open("slovene.pickle","wb")
95
- pickle.dump(tokenizer, out)
96
- out.close()
97
-
98
- ---------
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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nltk_data/tokenizers/punkt/README DELETED
@@ -1,98 +0,0 @@
1
- Pretrained Punkt Models -- Jan Strunk (New version trained after issues 313 and 514 had been corrected)
2
-
3
- Most models were prepared using the test corpora from Kiss and Strunk (2006). Additional models have
4
- been contributed by various people using NLTK for sentence boundary detection.
5
-
6
- For information about how to use these models, please confer the tokenization HOWTO:
7
- http://nltk.googlecode.com/svn/trunk/doc/howto/tokenize.html
8
- and chapter 3.8 of the NLTK book:
9
- http://nltk.googlecode.com/svn/trunk/doc/book/ch03.html#sec-segmentation
10
-
11
- There are pretrained tokenizers for the following languages:
12
-
13
- File Language Source Contents Size of training corpus(in tokens) Model contributed by
14
- =======================================================================================================================================================================
15
- czech.pickle Czech Multilingual Corpus 1 (ECI) Lidove Noviny ~345,000 Jan Strunk / Tibor Kiss
16
- Literarni Noviny
17
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
18
- danish.pickle Danish Avisdata CD-Rom Ver. 1.1. 1995 Berlingske Tidende ~550,000 Jan Strunk / Tibor Kiss
19
- (Berlingske Avisdata, Copenhagen) Weekend Avisen
20
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
21
- dutch.pickle Dutch Multilingual Corpus 1 (ECI) De Limburger ~340,000 Jan Strunk / Tibor Kiss
22
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
23
- english.pickle English Penn Treebank (LDC) Wall Street Journal ~469,000 Jan Strunk / Tibor Kiss
24
- (American)
25
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
26
- estonian.pickle Estonian University of Tartu, Estonia Eesti Ekspress ~359,000 Jan Strunk / Tibor Kiss
27
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
28
- finnish.pickle Finnish Finnish Parole Corpus, Finnish Books and major national ~364,000 Jan Strunk / Tibor Kiss
29
- Text Bank (Suomen Kielen newspapers
30
- Tekstipankki)
31
- Finnish Center for IT Science
32
- (CSC)
33
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
34
- french.pickle French Multilingual Corpus 1 (ECI) Le Monde ~370,000 Jan Strunk / Tibor Kiss
35
- (European)
36
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
37
- german.pickle German Neue Zürcher Zeitung AG Neue Zürcher Zeitung ~847,000 Jan Strunk / Tibor Kiss
38
- (Switzerland) CD-ROM
39
- (Uses "ss"
40
- instead of "ß")
41
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
42
- greek.pickle Greek Efstathios Stamatatos To Vima (TO BHMA) ~227,000 Jan Strunk / Tibor Kiss
43
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
44
- italian.pickle Italian Multilingual Corpus 1 (ECI) La Stampa, Il Mattino ~312,000 Jan Strunk / Tibor Kiss
45
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
46
- norwegian.pickle Norwegian Centre for Humanities Bergens Tidende ~479,000 Jan Strunk / Tibor Kiss
47
- (Bokmål and Information Technologies,
48
- Nynorsk) Bergen
49
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
50
- polish.pickle Polish Polish National Corpus Literature, newspapers, etc. ~1,000,000 Krzysztof Langner
51
- (http://www.nkjp.pl/)
52
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
53
- portuguese.pickle Portuguese CETENFolha Corpus Folha de São Paulo ~321,000 Jan Strunk / Tibor Kiss
54
- (Brazilian) (Linguateca)
55
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
56
- slovene.pickle Slovene TRACTOR Delo ~354,000 Jan Strunk / Tibor Kiss
57
- Slovene Academy for Arts
58
- and Sciences
59
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
60
- spanish.pickle Spanish Multilingual Corpus 1 (ECI) Sur ~353,000 Jan Strunk / Tibor Kiss
61
- (European)
62
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
63
- swedish.pickle Swedish Multilingual Corpus 1 (ECI) Dagens Nyheter ~339,000 Jan Strunk / Tibor Kiss
64
- (and some other texts)
65
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
66
- turkish.pickle Turkish METU Turkish Corpus Milliyet ~333,000 Jan Strunk / Tibor Kiss
67
- (Türkçe Derlem Projesi)
68
- University of Ankara
69
- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------
70
-
71
- The corpora contained about 400,000 tokens on average and mostly consisted of newspaper text converted to
72
- Unicode using the codecs module.
73
-
74
- Kiss, Tibor and Strunk, Jan (2006): Unsupervised Multilingual Sentence Boundary Detection.
75
- Computational Linguistics 32: 485-525.
76
-
77
- ---- Training Code ----
78
-
79
- # import punkt
80
- import nltk.tokenize.punkt
81
-
82
- # Make a new Tokenizer
83
- tokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()
84
-
85
- # Read in training corpus (one example: Slovene)
86
- import codecs
87
- text = codecs.open("slovene.plain","Ur","iso-8859-2").read()
88
-
89
- # Train tokenizer
90
- tokenizer.train(text)
91
-
92
- # Dump pickled tokenizer
93
- import pickle
94
- out = open("slovene.pickle","wb")
95
- pickle.dump(tokenizer, out)
96
- out.close()
97
-
98
- ---------
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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