33

我想要一个可以跨不同词性翻译/转换的python库函数。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，用于我想写的笔记程序。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）。

类似的问题...... 将形容词和副词转换为名词形式 （这个答案只源于词根。我想在词性之间切换。）

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

4

4 回答 4

21

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
于 2013-05-25T18:05:18.103 回答
12

这是一个理论上能够在名词/动词/形容词/副词形式之间转换单词的函数，我从这里更新（最初由bogs编写，我相信）现在符合 nltk 3.2.5synset.lemmas并且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，它的效果不是很好。它无法在形容词和副词形式之间切换（我的具体目标），但在其他情况下它确实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)]

希望这可以为某人省点麻烦

于 2018-01-12T00:57:44.410 回答
4

我知道这并不能回答您的全部问题，但确实回答了大部分问题。我会查看 http://nodebox.net/code/index.php/Linguistics#verb_conjugation 这个 Python 库能够结合动词，并识别一个词是动词、名词还是形容词。

示例代码

print en.verb.present("gave")
print en.verb.present("gave", person=3, negate=False)
>>> give
>>> gives

它还可以对单词进行分类。

print en.is_noun("banana")
>>> True

下载位于链接顶部。

于 2013-01-24T02:06:14.343 回答
3

一种方法可能是使用带有词性标签和词形映射的词词典。如果您获取或创建这样的字典（如果您可以访问任何传统字典的数据，这是很有可能的，因为所有字典都列出了单词的 POS 标签，以及所有派生形式的基本形式），您可以使用如下内容：

def is_verb(word):
    if word:
        tags = pos_tags(word)
        return 'VB' in tags or 'VBP' in tags or 'VBZ' in tags \
               or 'VBD' in tags or 'VBN' in tags:

def verbify(word):
    if is_verb(word):
        return word
    else:
       forms = []
       for tag in pos_tags(word):
           base = word_form(word, tag[:2])
           if is_verb(base):
              forms.append(base)
       return forms
于 2013-01-24T04:54:05.743 回答