importos, re,nltk
fromnltk.corpusimportwords, state_union,brown,treebank
fromcollectionsimportdefaultdict
列表与元组
# words = ['I', 'turned', 'off', 'the', 'spectroroute','the']
# words2=('I', 'turned', 'off', 'the', 'spectroroute','the','I')
# print (set(words))
# #print(reversed(words))
# print(sorted(words))
# print (set(words2))
# print(reversed(words2))
# print(sorted(words2))
#NOUN 名词
# brown_news_tagged=brown.tagged_words(categories='news',tagset='universal')
# word_tag_pairs=nltk.bigrams(brown_news_tagged)
# noun_proceders = [a[1]for(a,b)in word_tag_pairs if b[1]=='NOUN']
# fdist=nltk.FreqDist(noun_proceders)
# common_proceders=[tag for (tag,value) in fdist.most_common()]
# print(common_proceders) 获取名词前置的高频词类
#Verb 动词
获得过去分词以及过去式词形相同的动词
# wsj=treebank.tagged_words()
# cfd1=nltk.ConditionalFreqDist(wsj)
# vl=[w for w in cfd1.conditions()if 'VBN' in cfd1[w] and 'VBD' in cfd1[w]]
# print(vl)
获取某过去分词词以及其tag的位置
# cfd2=nltk.ConditionalFreqDist((tag,word)for (word,tag)in wsj)
# vbn_list=list(cfd2['VBN'])
# idx1=wsj.index(('kicked','VBN'))
# print(idx1)
获取其前置词
# for v in vbn_list:
# idx=wsj.index((v, 'VBN'))
# print (wsj[idx-1:idx])
等同于：
#print([wsj[wsj.index((v, 'VBN'))-1:wsj.index((v, 'VBN'))] for v in vbn_list])
#Ajectives and Adverbs 形容词和副词
词典反置是常用方法
# def findtags(tag_prefix, tagges_text):
# cfd=nltk.ConditionalFreqDist((tag,word) for (word,tag) in tagges_text
# if tag.startswith(tag_prefix))
# return dict((tag, cfd[tag].most_common(5) for tag in cfd.conditions()))
#exploring tagged corpora 探索标注的数据库
# brwon_learnd_tagged=brown.tagged_words(categories='learned', tagset='universal')
# tags=[b[1]for(a,b)in nltk.bigrams(brwon_learnd_tagged)if a[0]=='often']
# #print(tags)
# fd=nltk.FreqDist(tags)
# print(fd.tabulate())
# brwon_learnd_tagged=brown.tagged_words(categories='news', tagset='universal')
# cfd=nltk.ConditionalFreqDist((word.lower(),tag)
# for (word,tag) in brwon_learnd_tagged)
# for word in sorted(cfd.conditions()):
# if len(cfd[word])>3:
# tags=[tag for (tag, _) in cfd[word].most_common()]
# #print(cfd[word])
# print(word, tags)
#dictionary 词典：默认词典
# news_words = brown.words(categories='news')
# fd=nltk.FreqDist(news_words)
# v1000=[word for (word, _) in fd.most_common(1000)]
# mapping=defaultdict(lambda: 'UNK')
# for word in v1000:
# mapping[word]=word
# new_word=[mapping[word] for word in news_words]
# print(new_word[:20])
# incrementally updating a Dictionary 词典内容递增
# words = words.words('en')
# last_letters=defaultdict(list)
# for word in words:
# key=word[-2:] 发现有该类键，就将其名称以及值添加到字典中
# last_letters[key].append(word)
# print(last_letters['zy'][:10])
#
# anagrams=defaultdict(list) 找出有特定字母组成的所有的词
# for word in words:
# key=''.join(sorted(word))
# anagrams[key].append(word)
Nltk提供的简单方法
# anagrams=nltk.Index((''.join(sorted(w)),w)for w in words)
# print(anagrams['abc'])
#invert a dictionary 反置词典 便于查找
# pos={'cats':'N','name':'N','old':'ADJ','young':'ADJ','run':'V', 'sing':'V'}
# #pos2=dict((value,key)for (key,value)in pos.items())
# pos2=nltk.Index((value,key)for (key,value)in pos.items())
# print(pos2['N'])
#Automatic Tagging 自动标注: 用100个高频词汇的高频tag做tagger
#The Lookup Tagger 查找tagger
# brown_tagged_sents=brown.tagged_sents(categories='news')
# fd=nltk.FreqDist(brown.words(categories='news'))
# cfd=nltk.ConditionalFreqDist(brown.tagged_words(categories='news'))
# most_freq_words=fd.most_common(100)
# likely_tags=dict((word, cfd[word].max())for (word,_)in most_freq_words)
# baseline_tagger=nltk.UnigramTagger(model=likely_tags)
# print(cfd['news'].max())
# print(cfd['news'].tabulate())
# print(baseline_tagger.evaluate(brown_tagged_sents))
#N-Gram Tagging 多级标注
brown_tagged_sents=brown.tagged_sents(categories='news')
brown_sents=brown.sents(categories='news')
size=int(len(brown_tagged_sents)*0.9)
train_sents=brown_tagged_sents[:size] 将数据拆分
#print(train_sents[3])
test_sents=brown_tagged_sents[size:]
#
unigram_tagger=nltk.UnigramTagger(train_sents)
print(unigram_tagger.size())
#print(unigram_tagger.tag(brown_sents[3]))
#
# print(bigram_tagger.evaluate(test_sents))
#combination
# t0=nltk.DefaultTagger('NN')
# t1=nltk.UnigramTagger(train_sents, backoff=t0)
# t2=nltk.BigramTagger(train_sents, cutoff=2, backoff=t1)
#print(t2.evaluate(test_sents))
# test_tags = [tag for sent in brown.sents(categories='editorial')
# for (word, tag) in t2.tag(sent)]
# gold_tags = [tag for (word, tag) in brown.tagged_words(categories='editorial')]
# print(nltk.ConfusionMatrix(gold_tags, test_tags))
# cfd=nltk.ConditionalFreqDist(
# ((x[1],y[0]),y[1])
# for sent in brown_tagged_sents
# for x,y in nltk.bigrams(sent))
#
# ambigous_context=[c for c in cfd.conditions() if len(cfd[c])>1]
# print(sum(cfd[c].N()for c in ambigous_context)/cfd.N())
