Question

我想对这段文字进行词形推理，它只是将我需要的动词词汇化为lemmatize

    >>> import nltk, re, string
    >>> from nltk.stem import WordNetLemmatizer
    >>> from urllib import urlopen
    >>> url="https://raw.githubusercontent.com/evandrix/nltk_data/master/corpora/europarl_raw/english/ep-00-01-17.en"
    >>> raw = urlopen(url).read()
    >>> raw ="".join(l for l in raw if l not in string.punctuation)
    >>> tokens=nltk.word_tokenize(raw)
    >>> from nltk.stem import WordNetLemmatizer
    >>> lemmatizer = WordNetLemmatizer()
    >>> lem = [lemmatizer.lemmatize(t) for t in tokens]
    >>> lem[:20]
['Resumption', 'of', 'the', 'session', 'I', 'declare', 'resumed', 'the', 'session', 'of', 'the', 'European', 'Parliament', 'adjourned', 'on', 'Friday', '17', 'December', '1999', 'and']

这里的动词就像恢复一样它假设是简历你能告诉我应该做些什么来使整篇文章变得漂亮

Answer 1

使用wordnetlemmatizer中的pos参数：

>>> from nltk.stem import WordNetLemmatizer
>>> from nltk import pos_tag
>>> wnl = WordNetLemmatizer()
>>> wnl.lemmatize('resumed')
'resumed'
>>> wnl.lemmatize('resumed', pos='v')
u'resume'

这是一个完整的代码，pos_tag函数：

>>> from nltk import word_tokenize, pos_tag
>>> from nltk.stem import WordNetLemmatizer
>>> wnl = WordNetLemmatizer()
>>> txt = """Resumption of the session I declare resumed the session of the European Parliament adjourned on Friday 17 December 1999 , and I would like once again to wish you a happy new year in the hope that you enjoyed a pleasant festive period ."""
>>> [wnl.lemmatize(i,j[0].lower()) if j[0].lower() in ['a','n','v'] else wnl.lemmatize(i) for i,j in pos_tag(word_tokenize(txt))]
['Resumption', 'of', 'the', 'session', 'I', 'declare', u'resume', 'the', 'session', 'of', 'the', 'European', 'Parliament', u'adjourn', 'on', 'Friday', '17', 'December', '1999', ',', 'and', 'I', 'would', 'like', 'once', 'again', 'to', 'wish', 'you', 'a', 'happy', 'new', 'year', 'in', 'the', 'hope', 'that', 'you', u'enjoy', 'a', 'pleasant', 'festive', 'period', '.']

Python NLTK：如何将英文动词包含在动词中？

1 个答案: