python - nltk 的 RegexpParser 中的递归

Question

基于grammar in the chapter 7 of the NLTK Book :

grammar = r"""
      NP: {<DT|JJ|NN.*>+} # ...
"""

我想扩展 NP(名词短语)以包含由 CC 连接的多个 NP(并列连词:and) 或 ,(逗号)来捕获名词短语，例如:

• 房子和树
• 苹果、橙子和芒果
• 汽车、房子和飞机

我无法修改语法来将它们捕获为单个 NP:

import nltk

grammar = r"""
  NP: {<DT|JJ|NN.*>+(<CC|,>+<NP>)?}
"""

sentence = 'The house and tree'
chunkParser = nltk.RegexpParser(grammar)
words = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(words)
print(chunkParser.parse(tagged))

结果:

(S (NP The/DT house/NN) and/CC (NP tree/NN))

我试过将 NP 移到开头:NP: {(<NP><CC|,>+)?<DT|JJ|NN.*>+}但我得到了相同的结果

(S (NP The/DT house/NN) and/CC (NP tree/NN))

Answer 1

让我们从小事做起，正确地捕捉 NP(名词短语):

import nltk

grammar = r"""
  NP: {<DT|JJ|NN.*>+}
"""

sentence = 'The house and tree'
chunkParser = nltk.RegexpParser(grammar)
words = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(words)
print(chunkParser.parse(tagged))

[输出]:

(S (NP The/DT house/NN) and/CC (NP tree/NN))

现在让我们尝试捕捉 and/CC .只需添加一个更高级别的短语即可重新使用 <NP>规则:

grammar = r"""
  NP: {<DT|JJ|NN.*>+}
  CNP: {<NP><CC><NP>}
"""

sentence = 'The house and tree'
chunkParser = nltk.RegexpParser(grammar)
words = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(words)
print(chunkParser.parse(tagged))

[输出]:

(S (CNP (NP The/DT house/NN) and/CC (NP tree/NN)))

现在我们捕获了 NP CC NP短语，让我们花点时间看看它是否能捕获逗号:

grammar = r"""
  NP: {<DT|JJ|NN.*>+}
  CNP: {<NP><CC|,><NP>}
"""

sentence = 'The house, the bear and tree'
chunkParser = nltk.RegexpParser(grammar)
words = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(words)
print(chunkParser.parse(tagged))

现在我们看到它仅限于捕捉第一个左边界 NP CC|, NP并留下最后一个NP。

由于我们知道英语中的连词短语有左界连词和右界NP，即CC|, NP ，例如and the tree ，我们看到 CC|, NP模式是重复的，因此我们可以将其用作中间表示。

grammar = r"""
  NP: {<DT|JJ|NN.*>+}
  XNP: {<CC|,><NP>}
  CNP: {<NP><XNP>+}
"""

sentence = 'The house, the bear and tree'
chunkParser = nltk.RegexpParser(grammar)
words = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(words)
print(chunkParser.parse(tagged))

[输出]:

(S
  (CNP
    (NP The/DT house/NN)
    (XNP ,/, (NP the/DT bear/NN))
    (XNP and/CC (NP tree/NN))))

最终，CNP (连词 NPs)语法捕获英语中的链式名词短语连词，甚至是复杂的，例如

import nltk

grammar = r"""
  NP: {<DT|JJ|NN.*>+}
  XNP: {<CC|,><NP>}
  CNP: {<NP><XNP>+}
"""

sentence = 'The house, the bear, the green house and a tree went to the park or the river.'
chunkParser = nltk.RegexpParser(grammar)
words = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(words)
print(chunkParser.parse(tagged))

[输出]:

(S
  (CNP
    (NP The/DT house/NN)
    (XNP ,/, (NP the/DT bear/NN))
    (XNP ,/, (NP the/DT green/JJ house/NN))
    (XNP and/CC (NP a/DT tree/JJ)))
  went/VBD
  to/TO
  (CNP (NP the/DT park/NN) (XNP or/CC (NP the/DT river/NN)))
  ./.)

如果您只是想从 How to Traverse an NLTK Tree object? 中提取名词短语:

noun_phrases = []

def traverse_tree(tree):
    if tree.label() == 'CNP':
        noun_phrases.append(' '.join([token for token, tag in tree.leaves()]))
    for subtree in tree:
        if type(subtree) == nltk.tree.Tree:
            traverse_tree(subtree)

    return noun_phrases

sentence = 'The house, the bear, the green house and a tree went to the park or the river.'
chunkParser = nltk.RegexpParser(grammar)
words = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(words)
traverse_tree(chunkParser.parse(tagged))

[输出]:

['The house , the bear , the green house and a tree', 'the park or the river']

另请参阅 Python (NLTK) - more efficient way to extract noun phrases?