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python - nltk : How to prevent stemming of proper nouns

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

  • 通过除去字母
    • 除去任何内容来清理数据
  • 删除停用词
  • 阻止每个单词
  • 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，组织还是位置

    • 样例代码
    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')]

    如此明确，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，我将单词转换为小写并将其添加到将传递给NER的最终单词列表中。

    关于如何避免词干专有名词的任何想法？

    最佳答案

    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 token 生成器，清理器，POS标记器等。它不能与NER工具配合使用。

    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

    [出去]:
    <?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
    $ 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:
    >>> " ".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

    如果要使用NLTK随附的包装器，则必须等待一会儿，直到this issue解决； P

    关于python - nltk : How to prevent stemming of proper nouns，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/34455749/

    10-10 18:28
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