Python NLTK Lemductization of the word 'further' with wordnet

Question

我正在使用python、NLTK和WordNetLemmatizer开发一个lemmatizer。这是一个随机文本，输出我所期望的

from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet
lem = WordNetLemmatizer()
lem.lemmatize('worse', pos=wordnet.ADJ) // here, we are specifying that 'worse' is an adjective

输出：'bad'

lem.lemmatize('worse', pos=wordnet.ADV) // here, we are specifying that 'worse' is an adverb

输出：'worse'

这里一切都很好。行为与其他形容词相同，如'better'（表示不规则形式）或'older'（注意，与'elder'相同的测试永远不会输出'old'，但我猜wordnet并不是所有现有英语单词的详尽列表）

我的问题出现在尝试使用单词'furter':时

lem.lemmatize('further', pos=wordnet.ADJ) // as an adjective

输出：'further'

lem.lemmatize('further', pos=wordnet.ADV) // as an adverb

输出：'far'

这与'worse'单词的行为完全相反！

有人能解释一下为什么吗？这是来自wordnet-synsets数据的错误，还是来自我对英语语法的误解？

如果问题已经得到回答，请原谅，我在谷歌和SO上搜索过，但当指定关键字"进一步"时，我可以找到任何相关的东西，但由于这个词的流行，混乱。。。

提前感谢您，罗曼G.

Answer 1

WordNetLemmatizer使用._morphy函数来访问其a词引理；从…起http://www.nltk.org/_modules/nltk/stem/wordnet.html并返回具有最小长度的可能引理。

def lemmatize(self, word, pos=NOUN):
    lemmas = wordnet._morphy(word, pos)
    return min(lemmas, key=len) if lemmas else word

并且._morphy函数迭代地应用规则得到引理；规则不断减少单词的长度并用CCD_ 14替换词缀。然后它查看是否有其他单词更短，但与缩减后的单词相同：

def _morphy(self, form, pos):
    # from jordanbg:
    # Given an original string x
    # 1. Apply rules once to the input to get y1, y2, y3, etc.
    # 2. Return all that are in the database
    # 3. If there are no matches, keep applying rules until you either
    #    find a match or you can't go any further
    exceptions = self._exception_map[pos]
    substitutions = self.MORPHOLOGICAL_SUBSTITUTIONS[pos]
    def apply_rules(forms):
        return [form[:-len(old)] + new
                for form in forms
                for old, new in substitutions
                if form.endswith(old)]
    def filter_forms(forms):
        result = []
        seen = set()
        for form in forms:
            if form in self._lemma_pos_offset_map:
                if pos in self._lemma_pos_offset_map[form]:
                    if form not in seen:
                        result.append(form)
                        seen.add(form)
        return result
    # 0. Check the exception lists
    if form in exceptions:
        return filter_forms([form] + exceptions[form])
    # 1. Apply rules once to the input to get y1, y2, y3, etc.
    forms = apply_rules([form])
    # 2. Return all that are in the database (and check the original too)
    results = filter_forms([form] + forms)
    if results:
        return results
    # 3. If there are no matches, keep applying rules until we find a match
    while forms:
        forms = apply_rules(forms)
        results = filter_forms(forms)
        if results:
            return results
    # Return an empty list if we can't find anything
    return []

但是，如果该单词在异常列表中，则它将返回一个保留在exceptions中的固定值，请参阅中的_load_exception_maphttp://www.nltk.org/_modules/nltk/corpus/reader/wordnet.html:

def _load_exception_map(self):
    # load the exception file data into memory
    for pos, suffix in self._FILEMAP.items():
        self._exception_map[pos] = {}
        for line in self.open('%s.exc' % suffix):
            terms = line.split()
            self._exception_map[pos][terms[0]] = terms[1:]
    self._exception_map[ADJ_SAT] = self._exception_map[ADJ]

回到您的示例，worse->bad和further->far不能从规则中实现，因此必须从异常列表中实现。由于这是一个例外列表，所以必然会有不一致的地方。

异常列表保存在~/nltk_data/corpora/wordnet/adv.exc和~/nltk_data/corpora/wordnet/adv.exc中。

来自adv.exc:

best well
better well
deeper deeply
farther far
further far
harder hard
hardest hard

来自adj.exc:

...
worldliest worldly
wormier wormy
wormiest wormy
worse bad
worst bad
worthier worthy
worthiest worthy
wrier wry
...