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annotate hmm/semiSup.py @ 3:26d9c0308fcf

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updated/added from ecclerig version
author Henry S. Thompson <ht@inf.ed.ac.uk>
date Mon, 09 Mar 2020 17:35:28 +0000
parents e07789816ca5
children
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1 '''Exploring the claim that a small dictionary can seed
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2 an otherwise unsupervised HMM to learn a decent POS-tagger'''
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3 import nltk, random
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4 from nltk.corpus import brown
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5 from nltk.tag.hmm import HiddenMarkovModelTagger, HiddenMarkovModelTrainer
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6 from nltk.probability import FreqDist,ConditionalFreqDist
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7 from nltk.probability import MLEProbDist, RandomProbDist, DictionaryConditionalProbDist
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8
3
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9 trainTagsPercent=0.99
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10 trainHMMPercent=0.9
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11 knownWordsPercent=0.99
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12
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13 TAGSETS={
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14 'univ':
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15 [u'ADJ', u'ADP', u'ADV', u'CONJ', u'DET', u'NOUN', u'NUM',
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16 u'PRON', u'PRT', u'VERB', u'X', u'.'],
2
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17 'brown':
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18 [u"ABL", u"ABN", u"ABX", u"AP", u"AP$", u"AP+AP", u"AT", u"BE",
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19 u"BED", u"BED*", u"BEDZ", u"BEDZ*", u"BEG", u"BEM", u"BEM*",
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20 u"BEN", u"BER", u"BER*", u"BEZ", u"BEZ*", u"CC", u"CD",
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21 u"CD$", u"CS", u"DO", u"DO*", u"DO+PPSS", u"DOD", u"DOD*",
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22 u"DOZ", u"DOZ*", u"DT", u"DT$", u"DT+BEZ", u"DT+MD", u"DTI",
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23 u"DTS", u"DTS+BEZ", u"DTX", u"EX", u"EX+BEZ", u"EX+HVD", u"EX+HVZ",
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24 u"EX+MD", u"FW-*", u"FW-AT", u"FW-AT+NN", u"FW-AT+NP", u"FW-BE", u"FW-BER",
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25 u"FW-BEZ", u"FW-CC", u"FW-CD", u"FW-CS", u"FW-DT", u"FW-DT+BEZ", u"FW-DTS",
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26 u"FW-HV", u"FW-IN", u"FW-IN+AT", u"FW-IN+NN", u"FW-IN+NP", u"FW-JJ",
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27 u"FW-JJR", u"FW-JJT", u"FW-NN", u"FW-NN$", u"FW-NNS", u"FW-NP", u"FW-NPS",
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28 u"FW-NR", u"FW-OD", u"FW-PN", u"FW-PP$", u"FW-PPL", u"FW-PPL+VBZ",
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29 u"FW-PPO", u"FW-PPO+IN", u"FW-PPS", u"FW-PPSS", u"FW-PPSS+HV", u"FW-QL",
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30 u"FW-RB", u"FW-RB+CC", u"FW-TO+VB", u"FW-UH", u"FW-VB", u"FW-VBD",
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31 u"FW-VBG", u"FW-VBN", u"FW-VBZ", u"FW-WDT", u"FW-WPO", u"FW-WPS", u"HV",
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32 u"HV*", u"HV+TO", u"HVD", u"HVD*", u"HVG", u"HVN", u"HVZ", u"HVZ*", u"IN",
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33 u"IN+IN", u"IN+PPO", u"JJ", u"JJ$", u"JJ+JJ", u"JJR", u"JJR+CS", u"JJS",
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34 u"JJT", u"MD", u"MD*", u"MD+HV", u"MD+PPSS", u"MD+TO", u"NN", u"NN$",
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35 u"NN+BEZ", u"NN+HVD", u"NN+HVZ", u"NN+IN", u"NN+MD", u"NN+NN", u"NNS",
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36 u"NNS$", u"NNS+MD", u"NP", u"NP$", u"NP+BEZ", u"NP+HVZ", u"NP+MD",
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37 u"NPS", u"NPS$", u"NR", u"NR$", u"NR+MD", u"NRS", u"OD",
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38 u"PN", u"PN$", u"PN+BEZ", u"PN+HVD", u"PN+HVZ", u"PN+MD", u"PP$",
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39 u"PP$$", u"PPL", u"PPLS", u"PPO", u"PPS", u"PPS+BEZ", u"PPS+HVD",
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40 u"PPS+HVZ", u"PPS+MD", u"PPSS", u"PPSS+BEM", u"PPSS+BER", u"PPSS+BEZ",
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41 u"PPSS+BEZ*", u"PPSS+HV", u"PPSS+HVD", u"PPSS+MD", u"PPSS+VB", u"QL",
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42 u"QLP", u"RB", u"RB$", u"RB+BEZ", u"RB+CS", u"RBR", u"RBR+CS", u"RBT",
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43 u"RN", u"RP", u"RP+IN", u"TO", u"TO+VB", u"UH", u"VB", u"VB+AT",
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44 u"VB+IN", u"VB+JJ", u"VB+PPO", u"VB+RP", u"VB+TO", u"VB+VB", u"VBD",
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45 u"VBG", u"VBG+TO", u"VBN", u"VBN+TO", u"VBZ", u"WDT", u"WDT+BER",
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46 u"WDT+BER+PP", u"WDT+BEZ", u"WDT+DO+PPS", u"WDT+DOD", u"WDT+HVZ", u"WP$",
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47 u"WPO", u"WPS", u"WPS+BEZ", u"WPS+HVD", u"WPS+HVZ", u"WPS+MD", u"WQL",
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48 u"WRB", u"WRB+BER", u"WRB+BEZ", u"WRB+DO", u"WRB+DOD", u"WRB+DOD*",
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49 u"WRB+DOZ", u"WRB+IN", u"WRB+MD",
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50 u"(", u")", u"*", u",", u"--", u".", u":"],
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51 'upenn':
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52 [u"CC", u"CD", u"DT", u"EX", u"FW", u"IN", u"JJ", u"JJR", u"JJS", u"LS",
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53 u"MD", u"NN", u"NNP", u"NNPS", u"NNS", u"PDT", u"POS", u"PRP", u"PRP$",
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54 u"RB", u"RBR", u"RBS", u"RP", u"SYM", u"TO", u"UH", u"VB", u"VBD", u"VBG",
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55 u"VBN", u"VBP", u"VBZ", u"WDT", u"WP", u"WP$", u"WRB",
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56 u"``", u"$", u"''", u"(", u")", u",", u"--", u".", u":"]}
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57
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58 TAGSETS['universal']=TAGSETS['univ']
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59 TAGSETS['penn']=TAGSETS['upenn']
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60
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61 def setup(cat='news',tagset='brown',corpus=brown):
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62 return ([[(word.lower(),tag) for (word,tag) in s]
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63 for s in corpus.tagged_sents(categories=cat,tagset=tagset)],
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64 [(word.lower(),tag) for (word,tag) in corpus.tagged_words(categories=cat,tagset=tagset)],
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65 TAGSETS[tagset])
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66
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67 def notCurrent(s,missList):
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68 global i,n,done
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69 if done or (missList[i] is not s):
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70 return True
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71 else:
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72 i+=1
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73 if i==n:
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74 done=True
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75 return False
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76
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77 def splitData(words,wordPercent,sentences,sentPercent):
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78 global i,n, done
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79 trainWords=random.sample(words,int(wordPercent*len(words)))
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80 # random.sample(sentences,int(sentPercent*len(sentences)))
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81 trainSents=[s for s in sentences if random.random()<sentPercent]
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82 # hack!
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83 i=0
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84 n=len(trainSents)
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85 done=False
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86 testSents=[s for s in sentences if notCurrent(s,trainSents)]
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87 return trainWords, trainSents, testSents
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88
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89 def pickWords(tagged,percent):
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90 #wToT=ConditionalFreqDist(tagged)
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91 tToW=ConditionalFreqDist((t,w) for (w,t) in tagged)
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92 #print len(tToW[u'ADV'])
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93 return dict((tag,(lambda wl,p=percent:\
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94 wl[:int(p*len(wl))])(
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95 sorted(tToW[tag].items(),key=lambda (k,v):v,reverse=True)))
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96 for tag in tToW.keys())
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97
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98 (tagged_s,tagged_w,tagset)=setup(tagset='universal')
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99
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100 wordTokens=FreqDist(word for word,tag in tagged_w)
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101 print len(wordTokens), wordTokens.N()
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102
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103 (trainTags,trainHMM,testHMM)=splitData(tagged_w,trainTagsPercent,
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104 tagged_s,trainHMMPercent)
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105
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106 knownWords=pickWords(trainTags,knownWordsPercent)
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107
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108 class SubsetFreqDist(FreqDist):
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109 def __init__(self,pairs,baseset,basecount=.05):
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110 dict.update(self,pairs)
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111 self._baseset=baseset
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112 self._basecount=basecount
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113 pn=sum(n for w,n in pairs)
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114 self._N=pn+((len(baseset)-len(pairs))*basecount)
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115
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116 def __getitem__(self,key):
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117 return dict.__getitem__(self,key)
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118
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119 def __missing__(self,key):
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120 if key in self._baseset:
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121 return self._basecount
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122 else:
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123 return 0
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124
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125 def N(self):
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126 return self._N
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127
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128 class Tag:
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129 def __init__(self,tag,wordsAndCounts):
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130 self._tag=tag
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131 self._wordsAndCounts=wordsAndCounts
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132 self._words=set(w for w,n in wordsAndCounts)
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133 self._nTokens=sum(n for w,n in wordsAndCounts)
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134 self._nTypes=len(self._words)
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135
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136 def words(self):
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137 return self._words
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138
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139 def buildPD(self,tokens):
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140 self._sfd=SubsetFreqDist(self._wordsAndCounts,tokens)
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141 self._pd=MLEProbDist(self._sfd)
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142
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143 def getSFD(self):
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144 return self._sfd
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145
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146 def getPD(self):
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147 return self._pd
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148
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149 tags=dict((tagName,Tag(tagName,wl)) for tagName,wl in knownWords.items())
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150 kws=dict((tagName,tag.words()) for tagName,tag in tags.items())
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151
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152 t2=list(filter(None,
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153 ((lambda i:False if not i[1] else i)
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154 (((tagset[i],tagset[j]),
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155 kws[tagset[i]].intersection(kws[tagset[j]])),)
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156 for i in xrange(0,len(tagset))
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157 for j in xrange(i+1,len(tagset)))))
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158
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159 for tag in tags.values():
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160 tag.buildPD(wordTokens)
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161
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162 priors = RandomProbDist(tagset)
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163
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164 transitions = DictionaryConditionalProbDist(
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165 dict((state, RandomProbDist(tagset))
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166 for state in tagset))
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167
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168 outputs = DictionaryConditionalProbDist(
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169 dict((state, tags[state].getPD())
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170 for state in tagset))
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171
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172 model = HiddenMarkovModelTagger(wordTokens, tagset,
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173 transitions, outputs, priors)
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174
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175 print model.evaluate(testHMM)
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176
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177 nm=HiddenMarkovModelTrainer(states=tagset,symbols=wordTokens)
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178
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179 # Note that contrary to naive reading of the documentation,
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180 # train_unsupervised expects a sequence of sequences of word/tag pairs,
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181 # it just ignores the tags
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182 nnm=nm.train_unsupervised(trainHMM,model=model,max_iterations=15,updateOutputs=False)
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183
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184 print nnm.evaluate(testHMM)