python - 在动词/名词/形容词形式之间转换单词

Question

我想要一个 python 库函数，它可以跨不同的词性进行翻译/转换。有时它应该输出多个单词(例如“coder”和“code”都是动词“to code”的名词，一个是主语，另一个是宾语)

# :: String => List of String
print verbify('writer') # => ['write']
print nounize('written') # => ['writer']
print adjectivate('write') # => ['written']

我主要关心动词<=>名词，我想写一个笔记程序。即我可以写“咖啡因拮抗 A1”或“咖啡因是 A1 拮抗剂”，并且通过一些 NLP 它可以找出它们的意思相同。 (我知道这并不容易，它需要 NLP 来解析而不只是标记，但我想破解一个原型(prototype))。

类似的问题... Converting adjectives and adverbs to their noun forms(这个答案只能归结为根 POS。我想在 POS 之间切换。)

ps 在语言学中称为转换 http://en.wikipedia.org/wiki/Conversion_%28linguistics%29

Answer 1

这更像是一种启发式方法。我刚刚对它进行了编码，因此很适合这种风格。它使用来自 wordnet 的 derivationally_related_forms()。我已经实现了名词化。我猜verify的工作方式类似。从我测试的结果来看，效果很好:

from nltk.corpus import wordnet as wn

def nounify(verb_word):
    """ Transform a verb to the closest noun: die -> death """
    verb_synsets = wn.synsets(verb_word, pos="v")

    # Word not found
    if not verb_synsets:
        return []

    # Get all verb lemmas of the word
    verb_lemmas = [l for s in verb_synsets \
                   for l in s.lemmas if s.name.split('.')[1] == 'v']

    # Get related forms
    derivationally_related_forms = [(l, l.derivationally_related_forms()) \
                                    for l in    verb_lemmas]

    # filter only the nouns
    related_noun_lemmas = [l for drf in derivationally_related_forms \
                           for l in drf[1] if l.synset.name.split('.')[1] == 'n']

    # Extract the words from the lemmas
    words = [l.name for l in related_noun_lemmas]
    len_words = len(words)

    # Build the result in the form of a list containing tuples (word, probability)
    result = [(w, float(words.count(w))/len_words) for w in set(words)]
    result.sort(key=lambda w: -w[1])

    # return all the possibilities sorted by probability
    return result