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  1. nltk_data/tokenizers/punkt/PY3/README +98 -0
  2. nltk_data/tokenizers/punkt/PY3/czech.pickle +3 -0
  3. nltk_data/tokenizers/punkt/PY3/danish.pickle +3 -0
  4. nltk_data/tokenizers/punkt/PY3/dutch.pickle +3 -0
  5. nltk_data/tokenizers/punkt/PY3/english.pickle +3 -0
  6. nltk_data/tokenizers/punkt/PY3/estonian.pickle +3 -0
  7. nltk_data/tokenizers/punkt/PY3/finnish.pickle +3 -0
  8. nltk_data/tokenizers/punkt/PY3/french.pickle +3 -0
  9. nltk_data/tokenizers/punkt/PY3/german.pickle +3 -0
  10. nltk_data/tokenizers/punkt/PY3/greek.pickle +3 -0
  11. nltk_data/tokenizers/punkt/PY3/italian.pickle +3 -0
  12. nltk_data/tokenizers/punkt/PY3/malayalam.pickle +3 -0
  13. nltk_data/tokenizers/punkt/PY3/norwegian.pickle +3 -0
  14. nltk_data/tokenizers/punkt/PY3/polish.pickle +3 -0
  15. nltk_data/tokenizers/punkt/PY3/portuguese.pickle +3 -0
  16. nltk_data/tokenizers/punkt/PY3/russian.pickle +3 -0
  17. nltk_data/tokenizers/punkt/PY3/slovene.pickle +3 -0
  18. nltk_data/tokenizers/punkt/PY3/spanish.pickle +3 -0
  19. nltk_data/tokenizers/punkt/PY3/swedish.pickle +3 -0
  20. nltk_data/tokenizers/punkt/PY3/turkish.pickle +3 -0
  21. nltk_data/tokenizers/punkt/README +98 -0
  22. nltk_data/tokenizers/punkt/czech.pickle +3 -0
  23. nltk_data/tokenizers/punkt/danish.pickle +3 -0
  24. nltk_data/tokenizers/punkt/dutch.pickle +3 -0
  25. nltk_data/tokenizers/punkt/english.pickle +3 -0
  26. nltk_data/tokenizers/punkt/estonian.pickle +3 -0
  27. nltk_data/tokenizers/punkt/finnish.pickle +3 -0
  28. nltk_data/tokenizers/punkt/french.pickle +3 -0
  29. nltk_data/tokenizers/punkt/german.pickle +3 -0
  30. nltk_data/tokenizers/punkt/greek.pickle +3 -0
  31. nltk_data/tokenizers/punkt/italian.pickle +3 -0
  32. nltk_data/tokenizers/punkt/malayalam.pickle +3 -0
  33. nltk_data/tokenizers/punkt/norwegian.pickle +3 -0
  34. nltk_data/tokenizers/punkt/polish.pickle +3 -0
  35. nltk_data/tokenizers/punkt/portuguese.pickle +3 -0
  36. nltk_data/tokenizers/punkt/russian.pickle +3 -0
  37. nltk_data/tokenizers/punkt/slovene.pickle +3 -0
  38. nltk_data/tokenizers/punkt/spanish.pickle +3 -0
  39. nltk_data/tokenizers/punkt/swedish.pickle +3 -0
  40. nltk_data/tokenizers/punkt/turkish.pickle +3 -0
nltk_data/tokenizers/punkt/PY3/README ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ ---------
nltk_data/tokenizers/punkt/PY3/czech.pickle ADDED
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@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ ---------
nltk_data/tokenizers/punkt/czech.pickle ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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