因为现实中的数据多为‘非结构化数据’，比如一般的txt文档，或是‘半结构化数据’，比如html，对于这样的数据需要采用一些技术才能从中提取 出有用的信息。如果所有数据都是‘结构化数据’，比如Xml或关系数据库，那么就不需要特别去提取了，可以根据元数据去任意取到你想要的信息。
那么就来讨论一下用NLTK来实现文本信息提取的方法，
first, the raw text of the document is split into sentences using a sentence segmenter, and each sentence is further subdivided into words using a tokenizer . Next, each sentence is tagged with part-of-speech tags , which will prove very helpful in the next step,named entity recognition . In this step, we search for mentions of potentially interesting entities in each sentence. Finally, we use relation recognition to search for likely relations between different entities in the text.
可见这儿描述的信息提取的过程，包含4步，分词，词性标注，命名实体识别，实体关系识别，对于分词和词性标注前面已经介绍过了，那么就详细来看看named entity recognition 怎么来实现的。
Chunking
The basic technique we will use for entity recognition is chunking, which segments and labels multitoken sequences。
实体识别最基本的技术就是chunking，即分块，可以理解为把多个token组成词组。
Noun Phrase Chunking
我们就先以名词词组从chunking为例，即NP-chunking
One of the most useful sources of information for NP-chunking is part-of-speech tags.
>>> sentence = [("the", "DT"), ("little", "JJ"), ("yellow", "JJ"), ("dog", "NN"), ("barked", "VBD"), ("at", "IN"), ("the", "DT"), ("cat", "NN")]
>>> grammar = "NP: {<DT>?<JJ>*<NN>}" #Tag Patterns，定语（0或1个）形容词（任意个）名词（1个）
>>> cp = nltk.RegexpParser(grammar)
>>> result = cp.parse(sentence)
>>> print result
(S
(NP the/DT little/JJ yellow/JJ dog/NN) #NP-chunking, the little yellow dog
barked/VBD
at/IN
(NP the/DT cat/NN)) #NP-chunking, # NP-chunking, the cat
上面的这个方法就是用Regular Expressions来表示tag pattern，从而找到NP-chunking
再给个例子，tag patterns可以加上多条，可以变的更复杂
grammar = r"""NP: {<DT|PP/>?<JJ>*<NN>} # chunk determiner/possessive, adjectives and nouns {<NNP>+} # chunk sequences of proper nouns """ cp = nltk.RegexpParser(grammar) sentence = [("Rapunzel", "NNP"), ("let", "VBD"), ("down", "RP"), ("her", "PP>?<JJ>*<NN>} # chunk determiner/possessive, adjectives and nouns {<NNP>+} # chunk sequences of proper nouns """ cp = nltk.RegexpParser(grammar) sentence = [("Rapunzel", "NNP"), ("let", "VBD"), ("down", "RP"), ("her", "PP"), ("long", "JJ"), ("golden", "JJ"), ("hair", "NN")]
>>> print cp.parse(sentence)
(S
(NP Rapunzel/NNP) #NP-chunking, Rapunzel
let/VBD
down/RP
(NP her/PP$ long/JJ golden/JJ hair/NN)) #NP-chunking, her long golden hair
下面给个例子看看怎么从语料库中找到匹配的词性组合，
>>> cp = nltk.RegexpParser(''CHUNK: {<V.*> <TO> <V.*>}'') ＃找‘动词 to 动词’的组合
>>> brown = nltk.corpus.brown
>>> for sent in brown.tagged_sents():
... tree = cp.parse(sent)
... for subtree in tree.subtrees():
... if subtree.node == ''CHUNK'': print subtree
...
(CHUNK combined/VBN to/TO achieve/VB)
(CHUNK continue/VB to/TO place/VB)
(CHUNK serve/VB to/TO protect/VB)
(CHUNK wanted/VBD to/TO wait/VB)
(CHUNK allowed/VBN to/TO place/VB)
(CHUNK expected/VBN to/TO become/VB)
本文章摘自博客园，原文发布日期：2011-07-04