【语言处理与Python】5.1使用词性标注器/5.2标注语料库

什么是词性标注？

将词性按照它们的词性分类以及相应的标注它们的过程被成为词性标注。

词性也成为词类，或者词汇范畴。

用于特定任务的标记的集合被称为一个标记集。

5.1使用词性标注器

词性标注器的简单例子

text=nltk.word_tokenize(“And now for something completely different”)
nltk.pos_tag(text)

#查看词性

nltk.help.upenn_tagset(‘RB’)

5.2标注语料库

表示已标注的标识符

#转换成元组

tagged_token=nltk.tag.str2tuple(‘fly/NN’)

>>>sent = '''
... The/ATgrand/JJjury/NN commented/VBD on/INa/AT number/NNof/IN
... other/AP topics/NNS ,/, AMONG/INthem/PPO the/AT Atlanta/NPand/CC
... Fulton/NP-tlCounty/NN-tlpurchasing/VBG departments/NNSwhich/WDTit/PP
... said/VBD ``/`` ARE/BERwell/QLoperated/VBN and/CC follow/VB generally/R
... accepted/VBN practices/NNS which/WDTinure/VB to/IN the/AT best/JJT
... interest/NN of/INboth/ABX governments/NNS ''/'' ./.
... '''
>>>[nltk.tag.str2tuple(t) for t in sent.split()]
[('The', 'AT'), ('grand', 'JJ'), ('jury', 'NN'), ('commented', 'VBD'),
('on', 'IN'), ('a', 'AT'), ('number', 'NN'), ... ('.', '.')]

读取已经标注的语料库

#注意并非所有的语料库的标注都一致
nltk.corpus.brown.tagged_words()
[('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ...]
>>>nltk.corpus.brown.tagged_words(simplify_tags=True)
[('The', 'DET'), ('Fulton', 'N'), ('County', 'N'), ...]

简化的词性标记集

标记 含义 例子
ADJ 形容词 new,good, high, special, big, local
ADV 动词 really, already, still, early, now
CNJ  连词 and, or,but, if, while,although
DET  限定词 the, a, some, most,every, no
EX    存在量词 there, there's
FW   外来词 dolce, ersatz, esprit, quo,maitre
MOD 情态动词 will,can,would,may,must,should

N 名词 year,home, costs, time, education
NP 专有名词 Alison,Africa,April,Washington
NUM 数词 twenty-four, fourth, 1991,14:24
PRO 代词 he, their, her,its, my,I, us
P 介词 on, of,at, with,by,into, under
TO 词to to
UH 感叹词 ah, bang, ha,whee,hmpf,oops
V 动词 is, has,get, do,make,see, run
VD 过去式 said, took, told, made,asked
VG 现在分词 making,going, playing, working
VN 过去分词 given, taken, begun,sung
WH Wh限定词 who,which,when,what,where,how

名词

简化的名词标记对普通名词是N，对专有名词是NP

#如何构建一个双连词链表

brown_news_tagged=brown.tagged_words(categories='news', simplify_tags=True)
word_tag_pairs= nltk.bigrams(brown_news_tagged)
list(nltk.FreqDist(a[1] for (a, b)in word_tag_pairs if b[1]=='N'))
['DET', 'ADJ', 'N', 'P', 'NP', 'NUM', 'V', 'PRO', 'CNJ', '.', ',', 'VG', 'VN', ...]

这样就可以分析出，在一个名词前，会出现什么词性。

动词、形容词、副词（具体查看相关资料）

未简化的标记

让我们以一段程序开始，探索每个名词中最频繁的名词

NN

含有$的名词所有格

含有S的复数名词

含有P的专有名词

 

后缀修饰符

-NC表示引用

-HL表示标题中的词

-TL表示标题

 

def findtags(tag_prefix,tagged_text):
cfd=nltk.ConditionalFreqDist((tag,word) for (word,tag) in tagged_text if tag.startswith(tag_prefix))
return dict((tag,cfd[tag].keys()[:5]) for tag in cfd.condition())
>>>tagdict = findtags('NN', nltk.corpus.brown.tagged_words(categories='news'))
>>>for tag in sorted(tagdict):
... print tag, tagdict[tag]
...
NN['year', 'time', 'state', 'week', 'man']
NN$["year's", "world's", "state's", "nation's", "company's"]
NN$-HL["Golf's", "Navy's"]
NN$-TL["President's", "University's", "League's", "Gallery's", "Army's"]
NN-HL['cut', 'Salary', 'condition', 'Question', 'business']
NN-NC['eva', 'ova', 'aya']
NN-TL['President', 'House', 'State', 'University', 'City']
NN-TL-HL['Fort', 'City', 'Commissioner', 'Grove', 'House']
NNS['years', 'members', 'people', 'sales', 'men']
NNS$["children's", "women's", "men's", "janitors'", "taxpayers'"]
NNS$-HL["Dealers'", "Idols'"]
NNS$-TL["Women's", "States'", "Giants'", "Officers'", "Bombers'"]
NNS-HL['years', 'idols', 'Creations', 'thanks', 'centers']
NNS-TL['States', 'Nations', 'Masters', 'Rules', 'Communists']
NNS-TL-HL['Nations']

搜索已经标注的语料库

补充：

nltk.ibigrams()的用法示例：

>>> from nltk.util import ibigrams
>>> list(ibigrams([1,2,3,4,5]))
[(1, 2), (2, 3), (3, 4), (4, 5)]

查询之后，nltk.bigrams和nltk.ibigrams用法一致。

下面这个例子可以展示，如何研究often的用法，看看在often后面的词汇：

#这样查看，过于简单，意义也不大，如果结合词性看，更好
brown_learned_text = brown.words(categories='learned')
sorted(set(b for (a,b) in nltk.ibigrams(brown_learned_text) if a == 'often'))
#结合词性信息来研究often的用法
brown_lrnd_tagged=brown.tagged_words(categories='learned',dimplify_tags=True)
tags=[b[1] for (a,b) in nltk.ibigrams(brown_lrnd_tagged) if a[0] == 'often']
tags=nltk.FreqDist(tags)
fd.tabulate()

#使用POS标记寻找三词短语<Verb>到<Verb>

from nltk.corpusimport brown
def process(sentence):
for (w1,t1), (w2,t2), (w3,t3) in nltk.trigrams(sentence):
if (t1.startswith('V') and t2 =='TO' and t3.startswith('V')):
print w1,w2,w3
>>>for tagged_sent in brown.tagged_sents():
... process(tagged_sent)
...
combinedto achieve