Python - WordNet NLTK KeyError

Question

我真的不明白。我正在使用 WordNet，但遇到了麻烦。我用这样的字符串调用以下方法：

F2F - 流程开始，创建计划数据，创建收货，创建收货

def lemmatise(word, pos=NOUN):
        return WordNetLemmatizer().lemmatize(word, pos)

这个调用下一​​个方法：

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

morphy() 方法是错误说明问题的来源（参见错误消息）。

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
L1687   exceptions = self._exception_map[pos]
        substitutions = self.MORPHOLOGICAL_SUBSTITUTIONS[pos]

错误信息

[18/Nov/2016 15:02:57] "GET / HTTP/1.1" 200 4364
[18/Nov/2016 15:02:57] "GET /static/website/js/animation.js HTTP/1.1" 200 180
[18/Nov/2016 15:02:57] "GET /static/website/css/animation.css HTTP/1.1" 200 1069
[18/Nov/2016 15:02:57] "GET /static/website/img/mamegra_pro_thumbnail.png HTTP/1.1" 200 3588
[18/Nov/2016 15:02:57] "GET /static/website/css/bootstrap.min.css HTTP/1.1" 200 121260
[18/Nov/2016 15:02:59] "GET /syntactic_matching_final/ HTTP/1.1" 200 5571
[18/Nov/2016 15:03:02] "GET /semantic_matching_final/ HTTP/1.1" 200 5571
Internal Server Error: /semantic_matching_final/
Traceback (most recent call last):
  File "C:\Users\Bebop\AppData\Local\Programs\Python\Python35\lib\site-packages\django\core\handlers\exception.py", line 39, in inner
    response = get_response(request)
  File "C:\Users\Bebop\AppData\Local\Programs\Python\Python35\lib\site-packages\django\core\handlers\base.py", line 249, in _legacy_get_response
    response = self._get_response(request)
  File "C:\Users\Bebop\AppData\Local\Programs\Python\Python35\lib\site-packages\django\core\handlers\base.py", line 187, in _get_response
    response = self.process_exception_by_middleware(e, request)
  File "C:\Users\Bebop\AppData\Local\Programs\Python\Python35\lib\site-packages\django\core\handlers\base.py", line 185, in _get_response
    response = wrapped_callback(request, *callback_args, **callback_kwargs)
  File "C:\Users\Bebop\OneDrive\Masterarbeit\MamegraPro\MamegraProApp\views.py", line 418, in semantic_matching_final
    matches = matching.match_optimization('semantic_final', enable_infocontent) #perform syntactic matching and save matches in variable
  File "C:\Users\Bebop\OneDrive\Masterarbeit\MamegraPro\MamegraProApp\classes\class_matching.py", line 55, in match_optimization
    scores = {(n1, n2): self.semantic_score_final(n1.name, n2.name, enable_infocontent) if self.semantic_score_final(n1.name, n2.name, enable_infocontent) >= self.minimum_ratio else 0 for n1 in nodes1 for n2 in nodes2}
  File "C:\Users\Bebop\OneDrive\Masterarbeit\MamegraPro\MamegraProApp\classes\class_matching.py", line 55, in <dictcomp>
    scores = {(n1, n2): self.semantic_score_final(n1.name, n2.name, enable_infocontent) if self.semantic_score_final(n1.name, n2.name, enable_infocontent) >= self.minimum_ratio else 0 for n1 in nodes1 for n2 in nodes2}
  File "C:\Users\Bebop\OneDrive\Masterarbeit\MamegraPro\MamegraProApp\classes\class_matching.py", line 557, in semantic_score_final
    lemmas = self.corpusBase.lemmatise(w1)
  File "C:\Users\Bebop\OneDrive\Masterarbeit\MamegraPro\MamegraProApp\classes\class_wordnetWrapper.py", line 8, in lemmatise
    return WordNetLemmatizer().lemmatize(word, pos)
  File "C:\Users\Bebop\AppData\Local\Programs\Python\Python35\lib\site-packages\nltk\stem\wordnet.py", line 40, in lemmatize
    lemmas = wordnet._morphy(word, pos)
  File "C:\Users\Bebop\AppData\Local\Programs\Python\Python35\lib\site-packages\nltk\corpus\reader\wordnet.py", line 1687, in _morphy
    exceptions = self._exception_map[pos]
KeyError: 'Created'
[18/Nov/2016 15:03:11] "POST /semantic_matching_final/ HTTP/1.1" 500 122256
Not Found: /favicon.ico
[18/Nov/2016 15:03:12] "GET /favicon.ico HTTP/1.1" 404 4238

奇怪的是，即使输入相同，它也会随机切换引发错误的单词。请帮帮我！

Answer 1

检查以确保您传递的是一个字母而不是较长的字符串。这些可以在源 nltk.corpus.reader.wordnet 的常量部分中找到 { 词性常量 ADJ, ADJ_SAT, ADV, NOUN, VERB = 'a', 's', 'r', 'n', 'v' #}