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，可以在不同的语音部分之间进行翻译/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，因为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”，通过一些 NLP，它可以弄清楚它们的意思是一样的。 （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）。

类似的问题… 将形容词和副词转换为它们的名词形式（这个答案仅归结为根 POS。我想在 POS 之间进行。）

ps在语言学上叫做Conversion http://en.wikipedia.org/wiki/Conversion_%28linguistics%29

原文由 sam boosalis 发布，翻译遵循 CC BY-SA 4.0 许可协议

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()。我已经实施了名词化。我想 verbify 的工作方式类似。从我测试过的效果来看，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

原文由 bogs 发布，翻译遵循 CC BY-SA 3.0 许可协议

Answer 2

这是一个理论上能够在我从 这里 更新的名词/动词/形容词/副词形式之间转换单词的函数（我相信最初是由 bogs 编写的）现在符合 nltk 3.2.5 synset.lemmas 和 sysnset.name 是函数。

 from nltk.corpus import wordnet as wn

# Just to make it a bit more readable
WN_NOUN = 'n'
WN_VERB = 'v'
WN_ADJECTIVE = 'a'
WN_ADJECTIVE_SATELLITE = 's'
WN_ADVERB = 'r'

def convert(word, from_pos, to_pos):
    """ Transform words given from/to POS tags """

    synsets = wn.synsets(word, pos=from_pos)

    # Word not found
    if not synsets:
        return []

    # Get all lemmas of the word (consider 'a'and 's' equivalent)
    lemmas = []
    for s in synsets:
        for l in s.lemmas():
            if s.name().split('.')[1] == from_pos or from_pos in (WN_ADJECTIVE, WN_ADJECTIVE_SATELLITE) and s.name().split('.')[1] in (WN_ADJECTIVE, WN_ADJECTIVE_SATELLITE):
                lemmas += [l]

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

    # filter only the desired pos (consider 'a' and 's' equivalent)
    related_noun_lemmas = []

    for drf in derivationally_related_forms:
        for l in drf[1]:
            if l.synset().name().split('.')[1] == to_pos or to_pos in (WN_ADJECTIVE, WN_ADJECTIVE_SATELLITE) and l.synset().name().split('.')[1] in (WN_ADJECTIVE, WN_ADJECTIVE_SATELLITE):
                related_noun_lemmas += [l]

    # 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

convert('direct', 'a', 'r')
convert('direct', 'a', 'n')
convert('quick', 'a', 'r')
convert('quickly', 'r', 'a')
convert('hunger', 'n', 'v')
convert('run', 'v', 'a')
convert('tired', 'a', 'r')
convert('tired', 'a', 'v')
convert('tired', 'a', 'n')
convert('tired', 'a', 's')
convert('wonder', 'v', 'n')
convert('wonder', 'n', 'a')

正如您在下面看到的，它并不是那么好用。它无法在形容词和副词形式之间切换（我的具体目标），但它确实在其他情况下给出了一些有趣的结果。

 >>> convert('direct', 'a', 'r')
[]
>>> convert('direct', 'a', 'n')
[('directness', 0.6666666666666666), ('line', 0.3333333333333333)]
>>> convert('quick', 'a', 'r')
[]
>>> convert('quickly', 'r', 'a')
[]
>>> convert('hunger', 'n', 'v')
[('hunger', 0.75), ('thirst', 0.25)]
>>> convert('run', 'v', 'a')
[('persistent', 0.16666666666666666), ('executive', 0.16666666666666666), ('operative', 0.16666666666666666), ('prevalent', 0.16666666666666666), ('meltable', 0.16666666666666666), ('operant', 0.16666666666666666)]
>>> convert('tired', 'a', 'r')
[]
>>> convert('tired', 'a', 'v')
[]
>>> convert('tired', 'a', 'n')
[('triteness', 0.25), ('banality', 0.25), ('tiredness', 0.25), ('commonplace', 0.25)]
>>> convert('tired', 'a', 's')
[]
>>> convert('wonder', 'v', 'n')
[('wonder', 0.3333333333333333), ('wonderer', 0.2222222222222222), ('marveller', 0.1111111111111111), ('marvel', 0.1111111111111111), ('wonderment', 0.1111111111111111), ('question', 0.1111111111111111)]
>>> convert('wonder', 'n', 'a')
[('curious', 0.4), ('wondrous', 0.2), ('marvelous', 0.2), ('marvellous', 0.2)]

希望这能为某人省去一些麻烦

原文由 stuart 发布，翻译遵循 CC BY-SA 3.0 许可协议