-
Notifications
You must be signed in to change notification settings - Fork 4
/
crf_learn.py
807 lines (714 loc) · 25.1 KB
/
crf_learn.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
# this file is outdated, please refer to cv version
# crf_learn.py
# generate crfpp input
# input: annotated sentences
# Yiping 2012
import os
from multiprocessing import Pool
import re
import nltk
import math
import random
from nltk.corpus import conll2000
from nltk.chunk import ne_chunk
unigram_lm = {}
#keyphrase dictionary
kp_dictionary = {}
term_dictionary = {}
definition_dictionary = {}
patterns = []
#negative feature: containing pronoun
pronouns = []
#ngrams from wcl positive corpus
_4grams = []
_5grams = []
_6grams = []
_7grams = []
_8grams = []
# original:
# columns: word | postag | stemmed lexical | suffix | in term dictionary |
# in definition dictionary | capitalized | all caps | mixed case |
# captilized character with period (H.) | ends in digit | contains hyphen |
# len of word |
# 22/07/2012:
# in keyphrase dictionary | distance to beginning of document | len of sentence (# words) |
# pattern type | distance to pattern | first word | head word | NER chunktag |
# (other structural features)
# chunktag
class BigramChunker(nltk.ChunkParserI):
def __init__(self, train_sents):
train_data = [[(t,c) for w,t,c in nltk.chunk.tree2conlltags(sent)] for sent in train_sents]
self.tagger = nltk.BigramTagger(train_data)
def parse(self, sentence):
pos_tags = [pos for (word,pos) in sentence]
tagged_pos_tags = self.tagger.tag(pos_tags)
chunktags = [chunktag for (pos, chunktag) in tagged_pos_tags]
conlltags = [(word, pos, chunktag) for ((word,pos),chunktag) in zip(sentence, chunktags)]
#return nltk.chunk.conlltags2tree(conlltags)
return conlltags
train_sents = conll2000.chunked_sents('train.txt',chunk_types=['NP'])
np_chunker = BigramChunker(train_sents)
#data[0]:metadata data[1]:word data[2]:pos data[3]:chunktag data[4]:outtag data[5]:shallowParse data[6]:NE
def Map(data):
line = data[1]
section_header = "OUT"
section_id = "-1"
sent_id = -1
#sent index within section
sent_id_in_sec= -1
parts = data[0].split(" $ ")
if(len(parts)==5):
section_header = parts[1]
section_id = parts[2]
sent_id = parts[3]
#sent index within section
sent_id_in_sec= parts[4]
#initialize results
results = []
#need to tokenize here
words = line.split(" ")
#whether the sentence contains pronouns
has_pronoun = 0
for pronoun in pronouns:
pronoun = pronoun.strip()
matchStr = "\W?"+pronoun+"\W"
match = re.compile(matchStr)
if(len(match.findall(line))>0):
has_pronoun = 1
#has i.e.
has_ie = '0'
match = re.compile('i\.e\..{5,100}')
if(len(match.findall(line))>0):
has_ie = '1'
pattern_type = -1
pattern_index = -1
#determine whether the sentence contains a pattern
pattern_lookup = ["ABSENT","ABSENT","ABSENT","ABSENT","ABSENT","ABSENT","ABSENT","ABSENT","ABSENT","ABSENT","ABSENT"]
for kk in range(len(patterns)):
pattern = patterns[kk]
#process the regular expression
#find all the place holders
pattern = pattern.replace("\n","",1)
#determine the type of pattern
#type 1: <term> <pattern> <definition>
#type 2: <definition> <pattern> <term>
#ptype = pattern[1:2]
ptype = str(kk)
splitter = re.compile('\s?<.*?>,?\s?')
segments = splitter.split(pattern)
#the matchStr to look for
matchStr ='.{2,50} '
for segment in segments:
# to avoid the special cases where single space, comma or some
# other characters are parsed as a segment
if (len(segment)>=2 or (len(segment)==1 and segment.count('n')== 0)):
matchStr += segment +' .{2,80} '
match = re.compile(matchStr)
occurances = match.findall(line)
if(len(occurances)>0):
#print(occurance)
#pattern_type = ptype
#pattern_index = len(nltk.word_tokenize(line[:line.index(segments[2])]))
pattern_lookup[kk] = "PRESENT"
if(len(words)>=1):
first_word = words[0].lower()
else:
first_word = "NONE"
sentence_len = len(words)
'''
if(len(tokens)>5):
sentence_index = int(tokens[0])
sentence_len = int(tokens[1])
pattern_type = tokens[2]
#the index of the pattern in the string
pattern_index = int(tokens[3])
first_word = tokens[4].split('/')[0].lower()
else:
print('The line is too short (below 3 tokens)')
'''
#term frequency, extracted from input file
#tfs = []
#chunk tags BIO + Term / Def
'''
lastTag = "O"
for i in range(len(tokens)):
#elements = tokens[i].split('/')
index = tokens[i].rfind('/')
lastToken = tokens[i][index+1:]
if(lastToken == "TERM"):
chunkTags.append('TERM')
words.append(tokens[i][:index])
lastTag = "TERM"
elif(lastToken == "DEF"):
chunkTags.append('DEF')
words.append(tokens[i][:index])
lastTag = "DEF"
elif(lastToken != "O" and lastTag == "DEF" ):#the chunkTag not specified
chunkTags.append('DEF')
words.append(tokens[i])
elif(lastToken == "O"):
chunkTags.append('O')
words.append(tokens[i][:index])
lastTag = "O"
else:
chunkTags.append('O')
words.append(tokens[i])
lastTag = "O"
'''
outputTags = data[4].split(" ")
if(len(outputTags)!=len(words)):
print(str(len(outputTags))+":"+str(len(words)))
#if(len(outputTags)<len(words)):
#outputTags.append("O")
#pos tagging
#poss = nltk.pos_tag(words)
postags = data[2].split(" ")
#np_parsed = np_chunker.parse(poss)
shallowtags = data[3].split(" ")
ne_tags = data[6].split(" ")
##########################
##Shallow Parse Features##
parse_str = data[5]
short_str = parse_str.replace("ADVP","")
short_str = short_str.replace("ADJP","")
short_str = short_str.strip()
#short_list = short_str.split(' ')
#print(parse_str)
starts_with_NP = '0'
starts_with_ADVP = '0'
starts_with_PPNP = '0'
starts_with_DTNP = '0'
# NP : NP
has_NPiNP = '0'
# NP is
has_NPIS = '0'
# NP is * NP
has_NPISNP = '0'
# refer(s) to * NP
has_refer_to = '0'
# NP is * NP + of/that/which/for/and/PUNCTUATION
has_NPISNPPP = '0'
# NP or NP
has_NPorNP = '0'
# known as * NP
has_known_as = '0'
# NP of * NP
has_NPofNP = '0'
# NP ( NP )
has_NPBRNPBR = '0'
# NP a NP
has_NPaNP = '0'
has_NPconsistNP = '0'
has_NPdefineNP = '0'
#look up patterns appeared in wcl corpus
in_4gram = '0'
in_5gram = '0'
in_6gram = '0'
in_7gram = '0'
in_8gram = '0'
if(short_str.startswith('NP')):
starts_with_NP = 'X'
if(parse_str.startswith('ADVP') and 'NP' in parse_str[:10]):
starts_with_ADVP = 'X'
if(short_str.startswith('PP NP')):
starts_with_PPNP = 'X'
if(short_str.startswith('the NP') or short_str.startswith('a NP') or short_str.startswith('an NP')):
starts_with_DTNP = 'X'
regex = re.compile("NP (is|are|was|were) ")
if(len(regex.findall(short_str))>0):
has_NPIS = 'X'
regex = re.compile("NP : (the |The |a |A |an |An )?NP")
if(len(regex.findall(short_str))>0):
has_NPiNP = 'X'
regex = re.compile("NP (is|are|was|were) (the |a |any |some|an )?NP")
if(len(regex.findall(short_str))>0):
has_NPISNP = 'X'
regex = re.compile("(refer|refers) to (the |The |a |A |an |An )?NP")
if(len(regex.findall(short_str))>0):
has_refer_to = 'X'
regex = re.compile("NP (is|are|was|were) (the |a |any |some|an )?NP (of|that|which|for)")
if(len(regex.findall(short_str))>0):
has_NPISNPPP = 'X'
regex = re.compile("NP (or|,) (the )?NP")
if(len(regex.findall(short_str))>0):
has_NPorNP = 'X'
regex = re.compile(" known as (the )?NP")
if(len(regex.findall(short_str))>0):
has_known_as = 'X'
regex = re.compile("NP of (the |a )?NP")
if(len(regex.findall(short_str))>0):
has_NPofNP = 'X'
regex = re.compile("NP \( .{0,5}NP \)")
if(len(regex.findall(short_str))>0):
has_NPBRNPBR = 'X'
regex = re.compile("NP (the|a|any|some|an) NP")
if(len(regex.findall(short_str))>0):
has_NPaNP = 'X'
regex = re.compile("NP (consist|consists) .{0,5}(the |a |any |some|an )?NP")
if(len(regex.findall(short_str))>0):
has_NPconsistNP = 'X'
regex = re.compile("(NP )?(defined|Defined) (as |by )?(the |a |any |some|an )?NP")
if(len(regex.findall(short_str))>0):
has_NPdefineNP = 'X'
#print("Found: "+ short_str)
for _4gram in _4grams:
if _4gram.strip()[:-3] in short_str:
in_4gram = 'N'
for _5gram in _5grams:
if _5gram.strip()[:-3] in short_str:
in_5gram = 'N'
for _6gram in _6grams:
if _6gram.strip()[:-3] in short_str:
in_6gram = 'N'
for _7gram in _7grams:
if _7gram[:-3] in short_str:
in_7gram = 'N'
for _8gram in _8grams:
if _8gram[:-3] in short_str:
in_8gram = 'N'
##Shallow Parse Features##
##########################
#TODO: this can be useful, returns tree structure
#ne_parsed = ne_chunk(poss)
#postags = [pos for (word,pos) in poss]
#chunktags_np = [chunktag for (word,pos,chunktag) in np_parsed]
#chunktags_ne = [chunktag for (word,chunktag) in ne_parsed]
started = True
temp_result = []
#general purpose stemmer
porter = nltk.PorterStemmer()
#current index in the original str
current_index = 0
#obtain the chunk tags
for i in range(len(words)):
#store the features for current word in a dictionary
all_features = {}
#stemmed word
stem = porter.stem(words[i])
#suffix (if ends with ...)
if words[i].endswith('ion') or words[i].endswith('ity') or words[i].endswith('tor') or words[i].endswith('ics') or words[i].endswith('ment') or words[i].endswith('ive') or words[i].endswith('ic'):
suffix = 1
else:
suffix = 0
#look up term dictionary
if term_dictionary.has_key(words[i]) or term_dictionary.has_key(words[i].title()):
in_term_dic = 1
else:
in_term_dic = 0
#look up definition dictionary
if definition_dictionary.has_key(words[i]) or definition_dictionary.has_key(words[i].title()):
in_def_dic = 1
else:
in_def_dic = 0
#look up keyphrase dictionary
if kp_dictionary.has_key(words[i]) or kp_dictionary.has_key(words[i].title()):
in_kp_dic = 1
else:
in_kp_dic = 0
#below are all shape features
capitalized = 0
all_caps = 0
mixed_cases = 0
cap_with_period = 0
with_digit = 0
hyphen = 0
length = words[i].__len__()
capitalizedLetter = re.compile('[A-Z]')
digit = re.compile('[0-9]')
#captalized
if capitalizedLetter.search(words[i][0:1]):
capitalized = 1
#all captalized
if length > 1 and capitalizedLetter.search(words[i][1:]):
all_caps = 1
#some letter is captalized, not first one
elif length >1 and capitalizedLetter.search(words[i][1:]):
mixed_cases = 1
#last character '.' and last but one is capitalized
if length >2 and capitalizedLetter.search(words[i][length-2:]) and words[i][length-1:length] == '\.':
cap_with_period = 1
#contains digit
if digit.search(words[i]):
with_digit = 1
#contains hyphen
if '-' in words[i]:
hyphen = 1
#the relative position with the pattern
relative_pos = current_index - pattern_index
#calculate tf.idf feature. need normalizing to integer value
if unigram_lm.has_key(words[i]):
#TODO:the number of documents hard coded as 10000
idf = math.log(10000.0/unigram_lm[words[i]])
#smoothing assume occur once
else:
idf = math.log(1000000)
#normalize idf
if(idf<-5):
normalized_idf = '0'
elif(idf>=-5 and idf<-2):
normalized_idf = '1'
elif(idf>=-2 and idf<0):
normalized_idf = '2'
elif(idf>=0 and idf<1):
normalized_idf = '3'
elif(idf>=1 and idf<2):
normalized_idf = '4'
elif(idf>=2 and idf<3):
normalized_idf = '5'
elif(idf>=3 and idf<6):
normalized_idf = '6'
else:
normalized_idf = '7'
#if the word is in the first 10 words of the sentence, for term classifier
if(i<10 and i<0.4*len(words)):
first_10_word = '1'
else:
first_10_word = '0'
#if the word is part of a NP
if(shallowtags[i].endswith('NP')):
is_NP = '1'
else:
is_NP = '0'
if(is_NP=='1' and first_10_word=='1'):
is_head_NP = '1'
else:
is_head_NP = '0'
#if the token is directly before 'is a' or 'is the'
before_pattern = '0'
if(i<len(words)-5):
context_str = ''
for k in range(5):
context_str += words[i+k] + ' '
if('is a' in context_str or 'is the' in context_str):
before_pattern = '1'
#print(context_str)
#store the value of the features in the dictionary
all_features['word'] = words[i]
all_features['pos'] = postags[i]
all_features['stem'] = stem
all_features['suffix'] = suffix
all_features['outputTag'] = outputTags[i]
all_features['in_term_dictionary'] = in_term_dic
all_features['in_definition_dictionary'] = in_def_dic
all_features['length'] = words[i].__len__()/10
all_features['capitalized'] = capitalized
all_features['all_caps'] = all_caps
all_features['mixed_cases'] = mixed_cases
all_features['cap_with_period'] = cap_with_period
all_features['with_digit'] = with_digit
all_features['hyphen'] = hyphen
#added on 23.07.2012
all_features['in_keyphrase_dictionary'] = in_kp_dic
all_features['sentence_length'] = sentence_len
all_features['first_word'] = first_word
for kk in range(len(pattern_lookup)):
all_features[str(kk)] = pattern_lookup[kk]
all_features['pattern_type'] = pattern_type
all_features['pattern_index'] = pattern_index
all_features['relative_pos'] = relative_pos
all_features['normalized_idf'] = normalized_idf
#added on 27.09.2012
all_features['shallow_tag'] = shallowtags[i]
all_features['word_position'] = i
#added on 26.10.2012
all_features['has_pronoun'] = has_pronoun
#added on 30 Nov 2012
all_features['section_id'] = section_id
all_features['section_header'] = section_header
all_features['sent_id'] = sent_id
all_features['sent_id_in_sec'] = sent_id_in_sec
#TODO:shallow parse features
all_features['starts_with_NP'] = starts_with_NP
all_features['starts_with_ADVP'] = starts_with_ADVP
all_features['starts_with_PPNP'] = starts_with_PPNP
all_features['starts_with_DTNP'] = starts_with_DTNP
# NP : NP
all_features['has_NPiNP'] = has_NPiNP
# NP is
all_features['has_NPIS'] = has_NPIS
# NP is * NP
all_features['has_NPISNP'] = has_NPISNP
# refer(s) to * NP
all_features['has_refer_to'] = has_refer_to
# NP is * NP + of/that/which/for/and/PUNCTUATION
all_features['has_NPISNPPP'] = has_NPISNPPP
# NP or NP
all_features['has_NPorNP'] = has_NPorNP
# known as * NP
all_features['has_known_as'] = has_known_as
# NP of * NP
all_features['has_NPofNP'] = has_NPofNP
# NP ( NP )
all_features['has_NPBRNPBR'] = has_NPBRNPBR
#added after analyzing errors 25/12/2012
all_features['has_NPaNP'] = has_NPaNP
all_features['has_NPconsistNP'] = has_NPconsistNP
all_features['has_NPdefineNP'] = has_NPdefineNP
all_features['has_ie'] = has_ie
all_features['in_4gram'] = in_4gram
all_features['in_5gram'] = in_5gram
all_features['in_6gram'] = in_6gram
all_features['in_7gram'] = in_7gram
all_features['in_8gram'] = in_8gram
#added on 4/1/2013 to build term&definition classifier
all_features['first_10_word'] = first_10_word
all_features['is_NP'] = is_NP
all_features['is_head_NP'] = is_head_NP
all_features['before_pattern'] = before_pattern
all_features['NE'] = ne_tags[i]
#print(all_features)
results.append(all_features)
current_index += words[i].__len__() + 1
#print(len(results))
return results
#TODO: merge the definitions for the same term from different documents
#this method is postponed because I haven't figure out how to get the term
def Reduce(docid_term_definition) :
tuples = []
return tuples
################################################
# Main Function #
################################################
if __name__ == '__main__' :
#Parameters
#training_path = './corpus/wikiDef/input/annotated' #location of the documents
#training_path = './corpus/wikiDef/final1000.annotated'
#training_path = './corpus/wcl_datasets/input/annotated'
training_path = './corpus/annotate/annotated'
workers = 8
print('reading stop words...')
#load the stopwords
f_stop = open('dictionary/stopwords')
stopwords = f_stop.readlines()
stopwords_str = ' '.join(stopwords)
print('read ' + str(len(stopwords)) + ' stopwords.' )
print('reading unigram language model...')
f_uni = open('./ngram/acl.wfreq')
unigram = f_uni.readlines()
for line in unigram:
elements = line.strip().split(' ')
if(len(elements)>=2):
entry = elements[0]
count = int(elements[1])
unigram_lm[entry] = count
print('read ' + str(len(unigram_lm)) + ' entries in unigram.')
#print(unigram_lm['this'])
#load the dictionary for keyphrases extracted by kea
f_kp = open('./dictionary/acl_keyphrase.txt')
kps = f_kp.readlines()
for kp in kps:
kp = kp.strip()
if kp not in stopwords_str:
kp_dictionary[kp] = 1
f_kp.close()
#load the patterns
f_pt = open('./pattern.txt')
patterns = f_pt.readlines()
f_pt.close()
#load the pronouns
f_pn = open('./pronoun.txt')
pronouns = f_pn.readlines()
f_pn.close()
#load chunk ngrams from wcl corpus
f = open('./corpus/wcl_datasets/ngram/4gram')
_4grams = f.readlines()
f.close()
f = open('./corpus/wcl_datasets/ngram/5gram')
_5grams = f.readlines()
f.close()
f = open('./corpus/wcl_datasets/ngram/6gram')
_6grams = f.readlines()
f.close()
f = open('./corpus/wcl_datasets/ngram/7gram')
_7grams = f.readlines()
f.close()
f = open('./corpus/wcl_datasets/ngram/8gram')
_8grams = f.readlines()
f.close()
#############################################################
##reading the corpus, including .word, .pos, .chunk and .meta .parse
f_word = open(training_path+".word")
#read all the lines and store them in a list
sents = f_word.readlines()
#file no longer needs to be open
f_word.close()
#pos tags
f_pos = open(training_path+".pos")
#read all the lines and store them in a list
poss = f_pos.readlines()
#file no longer needs to be open
f_pos.close()
# read the shallow parsing chunk for each word
f_chunk = open(training_path+".chunk")
#read all the lines and store them in a list
chunks = f_chunk.readlines()
#file no longer needs to be open
f_chunk.close()
#read the sentence and section information
f_meta = open(training_path+".meta")
#read all the lines and store them in a list
metas = f_meta.readlines()
#file no longer needs to be open
f_meta.close()
#read the output tag
f_tag = open(training_path+".tag")
#read all the lines and store them in a list
tags = f_tag.readlines()
#file no longer needs to be open
f_tag.close()
#read the shallow parse sequence
f_parse = open(training_path+".parse")
#read all the lines and store them in a list
parses = f_parse.readlines()
#file no longer needs to be open
f_parse.close()
#read the named entity sequence
f_ne = open(training_path+".ne")
#read all the lines and store them in a list
nes = f_ne.readlines()
#file no longer needs to be open
f_ne.close()
##reading the corpus, including .word, .pos, .chunk and .meta
#############################################################
'''
#load the dictionary for term and definition
f_term = open('./ngramtool/term.uni')
terms = f_term.readlines()
for term in terms:
entry = term.split(' ')
if entry[0].strip() not in stopwords_str:
term_dictionary[entry[0]] = entry[1]
f_term.close()
f_def = open('./ngramtool/definition.uni')
definitions = f_def.readlines()
for definition in definitions:
entry = definition.split(' ')
if entry[0].strip() not in stopwords_str:
definition_dictionary[entry[0]] = entry[1]
f_def.close()
#print(term_dictionary.keys())
'''
#allocate a thread pool
#pool = Pool(processes=workers)
#train_results = pool.map(Map,training)
train_results = []
for i in range(len(sents)):
data = []
data.append(metas[i].strip())
data.append(sents[i].strip())
data.append(poss[i].strip())
data.append(chunks[i].strip())
data.append(tags[i].strip())
data.append(parses[i].strip())
data.append(nes[i].strip())
train_results.append(Map(data))
#random.shuffle(train_results)
#############################################################
##reading the corpus, including .word, .pos, .chunk and .meta .parse
training_path = './corpus/semi-supervised/annotated'
f_word = open(training_path+".word")
#read all the lines and store them in a list
sents = f_word.readlines()
#file no longer needs to be open
f_word.close()
#pos tags
f_pos = open(training_path+".pos")
#read all the lines and store them in a list
poss = f_pos.readlines()
#file no longer needs to be open
f_pos.close()
# read the shallow parsing chunk for each word
f_chunk = open(training_path+".chunk")
#read all the lines and store them in a list
chunks = f_chunk.readlines()
#file no longer needs to be open
f_chunk.close()
#read the sentence and section information
f_meta = open(training_path+".meta")
#read all the lines and store them in a list
metas = f_meta.readlines()
#file no longer needs to be open
f_meta.close()
#read the output tag
f_tag = open(training_path+".tag")
#read all the lines and store them in a list
tags = f_tag.readlines()
#file no longer needs to be open
f_tag.close()
#read the shallow parse sequence
f_parse = open(training_path+".parse")
#read all the lines and store them in a list
parses = f_parse.readlines()
#file no longer needs to be open
f_parse.close()
#read the named entity sequence
f_ne = open(training_path+".ne")
#read all the lines and store them in a list
nes = f_ne.readlines()
#file no longer needs to be open
f_ne.close()
for i in range(len(sents)):
data = []
data.append(metas[i].strip())
data.append(sents[i].strip())
data.append(poss[i].strip())
data.append(chunks[i].strip())
data.append(tags[i].strip())
data.append(parses[i].strip())
data.append(nes[i].strip())
train_results.append(Map(data))
##reading the corpus, including .word, .pos, .chunk and .meta
#############################################################
print 'Found ' + str(len(train_results)) + ' lines.'
#print 'Found ' + str(len(sentence_results)) + 'sentences'
#list of features to be used
features =['word','pos','stem','suffix','capitalized','all_caps','mixed_cases','cap_with_period','with_digit','hyphen','length','in_keyphrase_dictionary','sentence_length','first_word','normalized_idf','shallow_tag','word_position','section_id','section_header','sent_id','sent_id_in_sec','pattern_type','pattern_index','has_pronoun','starts_with_NP','starts_with_ADVP','starts_with_PPNP','starts_with_DTNP','has_NPiNP','has_NPIS','has_NPISNP','has_refer_to','has_NPISNPPP','has_NPorNP','has_known_as','has_NPofNP','has_NPBRNPBR','has_NPaNP','has_NPconsistNP','has_NPdefineNP','has_ie','in_4gram','in_5gram','in_6gram','in_7gram','in_8gram','first_10_word','is_NP','is_head_NP','before_pattern']
for k in range(11):
features.append(str(k))
features.append('NE')
features.append('outputTag')
#f = open("./CRF++-0.57/fyp/experiments/wcl/wcl_full.data","w+")
f = open("./CRF++-0.57/fyp/experiments/acl-arc/annotate.data","w+")
for sentence in train_results:
for word in sentence:
is_complete = True
line = ''
for feature in features:
if ' ' in str(word[feature]):
word[feature] = '?'
if(len(str(word[feature]))<1):
#error extracting feature
word[feature] = '?'
line += str(word[feature]) + ' '
if(is_complete):
f.write(line + '\n')
f.write('\n')
f.close()
'''
f_train = open("./CRF++-0.57/fyp/experiments/wcl/train.data","w+")
f_test = open("./CRF++-0.57/fyp/experiments/wcl/test.data","w+")
for i in range(len(train_results)):
sentence = train_results[i]
for word in sentence:
is_complete = True
line = ''
for feature in features:
line += str(word[feature]) + ' '
if(len(str(word[feature]))<1):
#error extracting feature
is_complete = False
if(' ' in str(word[feature])):
print("contain space!"+feature + ":" + str(word[feature]))
is_complete = False
if(is_complete):
if(i<0.9*len(train_results)):
f_train.write(line + '\n')
else:
f_test.write(line+ '\n')
if(i<0.9*len(train_results)):
f_train.write('\n')
else:
f_test.write('\n')
f_train.close()
f_test.close()
'''