nltk:如何防止专有名词的词干 [英] nltk : How to prevent stemming of proper nouns
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问题描述
我正在尝试使用Stanford POS标记器和NER编写关键字提取程序.对于关键字提取，我只对专有名词感兴趣.这是基本方法
I am trying to wrote a keyword extraction program using Stanford POS taggers and NER. For keyword extraction, i am only interested in proper nouns. Here is the basic approach
- 通过除去字母以外的任何内容来清理数据
- 删除停用词
- 词根每个词
- 确定每个单词的POS标签
- 如果POS标签是一个名词，则将其提供给NER
- 然后，NER将确定单词是一个人，一个组织还是一个位置
示例代码
docText="'Jack Frost works for Boeing Company. He manages 5 aircraft and their crew in London"
words = re.split("\W+",docText)
stops = set(stopwords.words("english"))
#remove stop words from the list
words = [w for w in words if w not in stops and len(w) > 2]
# Stemming
pstem = PorterStemmer()
words = [pstem.stem(w) for w in words]
nounsWeWant = set(['NN' ,'NNS', 'NNP', 'NNPS'])
finalWords = []
stn = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz')
stp = StanfordPOSTagger('english-bidirectional-distsim.tagger')
for w in words:
if stp.tag([w.lower()])[0][1] not in nounsWeWant:
finalWords.append(w.lower())
else:
finalWords.append(w)
finalString = " ".join(finalWords)
print finalString
tagged = stn.tag(finalWords)
print tagged
这给了我
Jack Frost work Boe Compani manag aircraft crew London
[(u'Jack', u'PERSON'), (u'Frost', u'PERSON'), (u'work', u'O'), (u'Boe', u'O'), (u'Compani', u'O'), (u'manag', u'O'), (u'aircraft', u'O'), (u'crew', u'O'), (u'London', u'LOCATION')]
很明显，我不想阻止波音公司.也没有公司.我需要阻止这些单词，因为我的输入可能包含诸如Performing之类的术语.我已经看到NER会选择像Performing这样的单词作为专有名词，因此可以归类为Organization.因此，首先我将所有单词都摘下来，然后转换为小写.然后，我检查该词的POS标签是否为名词.如果是这样，我保持原样.如果没有，我会将单词转换为小写，然后将其添加到将传递给NER的最终单词列表中.
so clearly, i did not want Boeing to be stemmed. nor Company. I need to stem the words as my input might contain terms like Performing. I have seen that a word like Performing will be picked up by the NER as a proper noun and hence could be categorized as Organization. Hence, first i stem all the words and convert to lower case. Then i check to see if the POS tag of the word is a noun. If so, i keep it as is. If not, i convert the word to lower case and add it to the final word list that will be passed to the NER.
关于如何避免词干专有名词的任何想法?
Any idea on how to avoid stemming proper nouns?
推荐答案
使用完整的Stanford CoreNLP管道来处理您的NLP工具链.避免使用自己的令牌生成器，清理器，POS标记器等.它不能与NER工具配合使用.
Use the full Stanford CoreNLP pipeline to handle your NLP tool chain. Avoid your own tokenizer, cleaner, POS tagger, etc. It will not play well with the NER tool.
wget http://nlp.stanford.edu/software/stanford-corenlp-full-2015-12-09.zip
unzip http://nlp.stanford.edu/software/stanford-corenlp-full-2015-12-09.zip
cd stanford-corenlp-full-2015-12-09
echo "Jack Frost works for Boeing Company. He manages 5 aircraft and their crew in London" > test.txt
java -cp "*" -Xmx2g edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,ner,parse,dcoref -file test.txt
cat test.txt.out
[输出]:
<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet href="CoreNLP-to-HTML.xsl" type="text/xsl"?>
<root>
<document>
<sentences>
<sentence id="1">
<tokens>
<token id="1">
<word>Jack</word>
<lemma>Jack</lemma>
<CharacterOffsetBegin>0</CharacterOffsetBegin>
<CharacterOffsetEnd>4</CharacterOffsetEnd>
<POS>NNP</POS>
<NER>PERSON</NER>
<Speaker>PER0</Speaker>
</token>
<token id="2">
<word>Frost</word>
<lemma>Frost</lemma>
<CharacterOffsetBegin>5</CharacterOffsetBegin>
<CharacterOffsetEnd>10</CharacterOffsetEnd>
<POS>NNP</POS>
<NER>PERSON</NER>
<Speaker>PER0</Speaker>
</token>
<token id="3">
<word>works</word>
<lemma>work</lemma>
<CharacterOffsetBegin>11</CharacterOffsetBegin>
<CharacterOffsetEnd>16</CharacterOffsetEnd>
<POS>VBZ</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="4">
<word>for</word>
<lemma>for</lemma>
<CharacterOffsetBegin>17</CharacterOffsetBegin>
<CharacterOffsetEnd>20</CharacterOffsetEnd>
<POS>IN</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="5">
<word>Boeing</word>
<lemma>Boeing</lemma>
<CharacterOffsetBegin>21</CharacterOffsetBegin>
<CharacterOffsetEnd>27</CharacterOffsetEnd>
<POS>NNP</POS>
<NER>ORGANIZATION</NER>
<Speaker>PER0</Speaker>
</token>
<token id="6">
<word>Company</word>
<lemma>Company</lemma>
<CharacterOffsetBegin>28</CharacterOffsetBegin>
<CharacterOffsetEnd>35</CharacterOffsetEnd>
<POS>NNP</POS>
<NER>ORGANIZATION</NER>
<Speaker>PER0</Speaker>
</token>
<token id="7">
<word>.</word>
<lemma>.</lemma>
<CharacterOffsetBegin>35</CharacterOffsetBegin>
<CharacterOffsetEnd>36</CharacterOffsetEnd>
<POS>.</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
</tokens>
<parse>(ROOT (S (NP (NNP Jack) (NNP Frost)) (VP (VBZ works) (PP (IN for) (NP (NNP Boeing) (NNP Company)))) (. .))) </parse>
<dependencies type="basic-dependencies">
<dep type="root">
<governor idx="0">ROOT</governor>
<dependent idx="3">works</dependent>
</dep>
<dep type="compound">
<governor idx="2">Frost</governor>
<dependent idx="1">Jack</dependent>
</dep>
<dep type="nsubj">
<governor idx="3">works</governor>
<dependent idx="2">Frost</dependent>
</dep>
<dep type="case">
<governor idx="6">Company</governor>
<dependent idx="4">for</dependent>
</dep>
<dep type="compound">
<governor idx="6">Company</governor>
<dependent idx="5">Boeing</dependent>
</dep>
<dep type="nmod">
<governor idx="3">works</governor>
<dependent idx="6">Company</dependent>
</dep>
<dep type="punct">
<governor idx="3">works</governor>
<dependent idx="7">.</dependent>
</dep>
</dependencies>
<dependencies type="collapsed-dependencies">
<dep type="root">
<governor idx="0">ROOT</governor>
<dependent idx="3">works</dependent>
</dep>
<dep type="compound">
<governor idx="2">Frost</governor>
<dependent idx="1">Jack</dependent>
</dep>
<dep type="nsubj">
<governor idx="3">works</governor>
<dependent idx="2">Frost</dependent>
</dep>
<dep type="case">
<governor idx="6">Company</governor>
<dependent idx="4">for</dependent>
</dep>
<dep type="compound">
<governor idx="6">Company</governor>
<dependent idx="5">Boeing</dependent>
</dep>
<dep type="nmod:for">
<governor idx="3">works</governor>
<dependent idx="6">Company</dependent>
</dep>
<dep type="punct">
<governor idx="3">works</governor>
<dependent idx="7">.</dependent>
</dep>
</dependencies>
<dependencies type="collapsed-ccprocessed-dependencies">
<dep type="root">
<governor idx="0">ROOT</governor>
<dependent idx="3">works</dependent>
</dep>
<dep type="compound">
<governor idx="2">Frost</governor>
<dependent idx="1">Jack</dependent>
</dep>
<dep type="nsubj">
<governor idx="3">works</governor>
<dependent idx="2">Frost</dependent>
</dep>
<dep type="case">
<governor idx="6">Company</governor>
<dependent idx="4">for</dependent>
</dep>
<dep type="compound">
<governor idx="6">Company</governor>
<dependent idx="5">Boeing</dependent>
</dep>
<dep type="nmod:for">
<governor idx="3">works</governor>
<dependent idx="6">Company</dependent>
</dep>
<dep type="punct">
<governor idx="3">works</governor>
<dependent idx="7">.</dependent>
</dep>
</dependencies>
</sentence>
<sentence id="2">
<tokens>
<token id="1">
<word>He</word>
<lemma>he</lemma>
<CharacterOffsetBegin>37</CharacterOffsetBegin>
<CharacterOffsetEnd>39</CharacterOffsetEnd>
<POS>PRP</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="2">
<word>manages</word>
<lemma>manage</lemma>
<CharacterOffsetBegin>40</CharacterOffsetBegin>
<CharacterOffsetEnd>47</CharacterOffsetEnd>
<POS>VBZ</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="3">
<word>5</word>
<lemma>5</lemma>
<CharacterOffsetBegin>48</CharacterOffsetBegin>
<CharacterOffsetEnd>49</CharacterOffsetEnd>
<POS>CD</POS>
<NER>NUMBER</NER>
<NormalizedNER>5.0</NormalizedNER>
<Speaker>PER0</Speaker>
</token>
<token id="4">
<word>aircraft</word>
<lemma>aircraft</lemma>
<CharacterOffsetBegin>50</CharacterOffsetBegin>
<CharacterOffsetEnd>58</CharacterOffsetEnd>
<POS>NN</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="5">
<word>and</word>
<lemma>and</lemma>
<CharacterOffsetBegin>59</CharacterOffsetBegin>
<CharacterOffsetEnd>62</CharacterOffsetEnd>
<POS>CC</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="6">
<word>their</word>
<lemma>they</lemma>
<CharacterOffsetBegin>63</CharacterOffsetBegin>
<CharacterOffsetEnd>68</CharacterOffsetEnd>
<POS>PRP$</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="7">
<word>crew</word>
<lemma>crew</lemma>
<CharacterOffsetBegin>69</CharacterOffsetBegin>
<CharacterOffsetEnd>73</CharacterOffsetEnd>
<POS>NN</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="8">
<word>in</word>
<lemma>in</lemma>
<CharacterOffsetBegin>74</CharacterOffsetBegin>
<CharacterOffsetEnd>76</CharacterOffsetEnd>
<POS>IN</POS>
<NER>O</NER>
<Speaker>PER0</Speaker>
</token>
<token id="9">
<word>London</word>
<lemma>London</lemma>
<CharacterOffsetBegin>77</CharacterOffsetBegin>
<CharacterOffsetEnd>83</CharacterOffsetEnd>
<POS>NNP</POS>
<NER>LOCATION</NER>
<Speaker>PER0</Speaker>
</token>
</tokens>
<parse>(ROOT (S (NP (PRP He)) (VP (VBZ manages) (NP (NP (CD 5) (NN aircraft)) (CC and) (NP (NP (PRP$ their) (NN crew)) (PP (IN in) (NP (NNP London)))))))) </parse>
<dependencies type="basic-dependencies">
<dep type="root">
<governor idx="0">ROOT</governor>
<dependent idx="2">manages</dependent>
</dep>
<dep type="nsubj">
<governor idx="2">manages</governor>
<dependent idx="1">He</dependent>
</dep>
<dep type="nummod">
<governor idx="4">aircraft</governor>
<dependent idx="3">5</dependent>
</dep>
<dep type="dobj">
<governor idx="2">manages</governor>
<dependent idx="4">aircraft</dependent>
</dep>
<dep type="cc">
<governor idx="4">aircraft</governor>
<dependent idx="5">and</dependent>
</dep>
<dep type="nmod:poss">
<governor idx="7">crew</governor>
<dependent idx="6">their</dependent>
</dep>
<dep type="conj">
<governor idx="4">aircraft</governor>
<dependent idx="7">crew</dependent>
</dep>
<dep type="case">
<governor idx="9">London</governor>
<dependent idx="8">in</dependent>
</dep>
<dep type="nmod">
<governor idx="7">crew</governor>
<dependent idx="9">London</dependent>
</dep>
</dependencies>
<dependencies type="collapsed-dependencies">
<dep type="root">
<governor idx="0">ROOT</governor>
<dependent idx="2">manages</dependent>
</dep>
<dep type="nsubj">
<governor idx="2">manages</governor>
<dependent idx="1">He</dependent>
</dep>
<dep type="nummod">
<governor idx="4">aircraft</governor>
<dependent idx="3">5</dependent>
</dep>
<dep type="dobj">
<governor idx="2">manages</governor>
<dependent idx="4">aircraft</dependent>
</dep>
<dep type="cc">
<governor idx="4">aircraft</governor>
<dependent idx="5">and</dependent>
</dep>
<dep type="nmod:poss">
<governor idx="7">crew</governor>
<dependent idx="6">their</dependent>
</dep>
<dep type="conj:and">
<governor idx="4">aircraft</governor>
<dependent idx="7">crew</dependent>
</dep>
<dep type="case">
<governor idx="9">London</governor>
<dependent idx="8">in</dependent>
</dep>
<dep type="nmod:in">
<governor idx="7">crew</governor>
<dependent idx="9">London</dependent>
</dep>
</dependencies>
<dependencies type="collapsed-ccprocessed-dependencies">
<dep type="root">
<governor idx="0">ROOT</governor>
<dependent idx="2">manages</dependent>
</dep>
<dep type="nsubj">
<governor idx="2">manages</governor>
<dependent idx="1">He</dependent>
</dep>
<dep type="nummod">
<governor idx="4">aircraft</governor>
<dependent idx="3">5</dependent>
</dep>
<dep type="dobj">
<governor idx="2">manages</governor>
<dependent idx="4">aircraft</dependent>
</dep>
<dep type="cc">
<governor idx="4">aircraft</governor>
<dependent idx="5">and</dependent>
</dep>
<dep type="nmod:poss">
<governor idx="7">crew</governor>
<dependent idx="6">their</dependent>
</dep>
<dep type="dobj" extra="true">
<governor idx="2">manages</governor>
<dependent idx="7">crew</dependent>
</dep>
<dep type="conj:and">
<governor idx="4">aircraft</governor>
<dependent idx="7">crew</dependent>
</dep>
<dep type="case">
<governor idx="9">London</governor>
<dependent idx="8">in</dependent>
</dep>
<dep type="nmod:in">
<governor idx="7">crew</governor>
<dependent idx="9">London</dependent>
</dep>
</dependencies>
</sentence>
</sentences>
<coreference>
<coreference>
<mention representative="true">
<sentence>1</sentence>
<start>1</start>
<end>3</end>
<head>2</head>
<text>Jack Frost</text>
</mention>
<mention>
<sentence>2</sentence>
<start>1</start>
<end>2</end>
<head>1</head>
<text>He</text>
</mention>
</coreference>
</coreference>
</document>
</root>
或获取json输出:
java -cp "*" -Xmx2g edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,ner,parse,dcoref -file test.txt -outputFormat json
如果您确实需要python包装器，请参见 https://github.com/smilli/py- corenlp
And if you really need a python wrapper, see https://github.com/smilli/py-corenlp
$ cd stanford-corenlp-full-2015-12-09
$ export CLASSPATH=protobuf.jar:joda-time.jar:jollyday.jar:xom-1.2.10.jar:stanford-corenlp-3.6.0.jar:stanford-corenlp-3.6.0-models.jar:slf4j-api.jar
$ java -mx4g edu.stanford.nlp.pipeline.StanfordCoreNLPServer &
cd
$ git clone https://github.com/smilli/py-corenlp.git
$ cd py-corenlp
$ python
>>> from corenlp import StanfordCoreNLP
>>> nlp = StanfordCoreNLP('http://localhost:9000')
>>> text = ("Jack Frost works for Boeing Company. He manages 5 aircraft and their crew in London")
>>> output = nlp.annotate(text, properties={'annotators': 'tokenize,ssplit,pos,ner', 'outputFormat': 'json'})
>>> output
{u'sentences': [{u'parse': u'SENTENCE_SKIPPED_OR_UNPARSABLE', u'index': 0, u'tokens': [{u'index': 1, u'word': u'Jack', u'lemma': u'Jack', u'after': u' ', u'pos': u'NNP', u'characterOffsetEnd': 4, u'characterOffsetBegin': 0, u'originalText': u'Jack', u'ner': u'PERSON', u'before': u''}, {u'index': 2, u'word': u'Frost', u'lemma': u'Frost', u'after': u' ', u'pos': u'NNP', u'characterOffsetEnd': 10, u'characterOffsetBegin': 5, u'originalText': u'Frost', u'ner': u'PERSON', u'before': u' '}, {u'index': 3, u'word': u'works', u'lemma': u'work', u'after': u' ', u'pos': u'VBZ', u'characterOffsetEnd': 16, u'characterOffsetBegin': 11, u'originalText': u'works', u'ner': u'O', u'before': u' '}, {u'index': 4, u'word': u'for', u'lemma': u'for', u'after': u' ', u'pos': u'IN', u'characterOffsetEnd': 20, u'characterOffsetBegin': 17, u'originalText': u'for', u'ner': u'O', u'before': u' '}, {u'index': 5, u'word': u'Boeing', u'lemma': u'Boeing', u'after': u' ', u'pos': u'NNP', u'characterOffsetEnd': 27, u'characterOffsetBegin': 21, u'originalText': u'Boeing', u'ner': u'ORGANIZATION', u'before': u' '}, {u'index': 6, u'word': u'Company', u'lemma': u'Company', u'after': u'', u'pos': u'NNP', u'characterOffsetEnd': 35, u'characterOffsetBegin': 28, u'originalText': u'Company', u'ner': u'ORGANIZATION', u'before': u' '}, {u'index': 7, u'word': u'.', u'lemma': u'.', u'after': u' ', u'pos': u'.', u'characterOffsetEnd': 36, u'characterOffsetBegin': 35, u'originalText': u'.', u'ner': u'O', u'before': u''}]}, {u'parse': u'SENTENCE_SKIPPED_OR_UNPARSABLE', u'index': 1, u'tokens': [{u'index': 1, u'word': u'He', u'lemma': u'he', u'after': u' ', u'pos': u'PRP', u'characterOffsetEnd': 39, u'characterOffsetBegin': 37, u'originalText': u'He', u'ner': u'O', u'before': u' '}, {u'index': 2, u'word': u'manages', u'lemma': u'manage', u'after': u' ', u'pos': u'VBZ', u'characterOffsetEnd': 47, u'characterOffsetBegin': 40, u'originalText': u'manages', u'ner': u'O', u'before': u' '}, {u'index': 3, u'after': u' ', u'word': u'5', u'lemma': u'5', u'normalizedNER': u'5.0', u'pos': u'CD', u'characterOffsetEnd': 49, u'characterOffsetBegin': 48, u'originalText': u'5', u'ner': u'NUMBER', u'before': u' '}, {u'index': 4, u'word': u'aircraft', u'lemma': u'aircraft', u'after': u' ', u'pos': u'NN', u'characterOffsetEnd': 58, u'characterOffsetBegin': 50, u'originalText': u'aircraft', u'ner': u'O', u'before': u' '}, {u'index': 5, u'word': u'and', u'lemma': u'and', u'after': u' ', u'pos': u'CC', u'characterOffsetEnd': 62, u'characterOffsetBegin': 59, u'originalText': u'and', u'ner': u'O', u'before': u' '}, {u'index': 6, u'word': u'their', u'lemma': u'they', u'after': u' ', u'pos': u'PRP$', u'characterOffsetEnd': 68, u'characterOffsetBegin': 63, u'originalText': u'their', u'ner': u'O', u'before': u' '}, {u'index': 7, u'word': u'crew', u'lemma': u'crew', u'after': u' ', u'pos': u'NN', u'characterOffsetEnd': 73, u'characterOffsetBegin': 69, u'originalText': u'crew', u'ner': u'O', u'before': u' '}, {u'index': 8, u'word': u'in', u'lemma': u'in', u'after': u' ', u'pos': u'IN', u'characterOffsetEnd': 76, u'characterOffsetBegin': 74, u'originalText': u'in', u'ner': u'O', u'before': u' '}, {u'index': 9, u'word': u'London', u'lemma': u'London', u'after': u'', u'pos': u'NNP', u'characterOffsetEnd': 83, u'characterOffsetBegin': 77, u'originalText': u'London', u'ner': u'LOCATION', u'before': u' '}]}]}
>>> annotated_sent0 = output['sentences'][0]
>>> for token in annotated_sent0['tokens']:
... print token['word'], token['lemma'], token['pos'], token['ner']
...
Jack Jack NNP PERSON
Frost Frost NNP PERSON
works work VBZ O
for for IN O
Boeing Boeing NNP ORGANIZATION
Company Company NNP ORGANIZATION
. . . O
可能这是您想要的输出:
Possibly this is the output you want:
>>> " ".join(token['lemma'] for token in annotated_sent0['tokens'])
Jack Frost work for Boeing Company
>>> " ".join(token['word'] for token in annotated_sent0['tokens'])
Jack Frost works for Boeing Company
如果您想使用NLTK随附的包装器，则必须再等一会儿，直到这个问题已解决； P
If you want a wrapper that comes with NLTK, then you have to wait just a little longer until this issue is resolved ;P
这篇关于nltk:如何防止专有名词的词干的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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