元组没有属性“ isdigit”

我需要使用NLTK模块进行一些文字处理，然后出现以下错误：AttributeError：'tuple'对象没有属性'isdigit'

有人知道如何处理此错误吗？

Traceback (most recent call last):
  File "preprocessing-edit.py", line 36, in 
    postoks = nltk.tag.pos_tag(tok)
NameError: name 'tok' is not defined

PS C:\Users\moham\Desktop\Presentation> python preprocessing-edit.py
Traceback (most recent call last):
  File "preprocessing-edit.py", line 37, in 
    postoks = nltk.tag.pos_tag(tok)
  File "c:\python34\lib\site-packages\nltk-3.1-py3.4.egg\nltk\tag\__init__.py", line 111, in pos_tag
    return _pos_tag(tokens, tagset, tagger)
  File "c:\python34\lib\site-packages\nltk-3.1-py3.4.egg\nltk\tag\__init__.py", line 82, in _pos_tag
    tagged_tokens = tagger.tag(tokens)
  File "c:\python34\lib\site-packages\nltk-3.1-py3.4.egg\nltk\tag\perceptron.py", line 153, in tag
    context = self.START + [self.normalize(w) for w in tokens] + self.END
  File "c:\python34\lib\site-packages\nltk-3.1-py3.4.egg\nltk\tag\perceptron.py", line 153, in 
    context = self.START + [self.normalize(w) for w in tokens] + self.END
  File "c:\python34\lib\site-packages\nltk-3.1-py3.4.egg\nltk\tag\perceptron.py", line 228, in normalize
    elif word.isdigit() and len(word) == 4:
AttributeError: 'tuple' object has no attribute 'isdigit'

import nltk

with open ("SHORT-LIST.txt", "r",encoding='utf8') as myfile:
    text =  (myfile.read().replace('\n', ''))

#text = "program managment is complicated issue for human workers"

# Used when tokenizing words
sentence_re = r'''(?x)      # set flag to allow verbose regexps
      ([A-Z])(\.[A-Z])+\.?  # abbreviations, e.g. U.S.A.
    | \w+(-\w+)*            # words with optional internal hyphens
    | \$?\d+(\.\d+)?%?      # currency and percentages, e.g. $12.40, 82%
    | \.\.\.                # ellipsis
    | [][.,;"'?():-_`]      # these are separate tokens
'''

lemmatizer = nltk.WordNetLemmatizer()
stemmer = nltk.stem.porter.PorterStemmer()


grammar = r"""
    NBAR:
        {*}  # Nouns and Adjectives, terminated with Nouns

    NP:
        {}
        {}  # Above, connected with in/of/etc...
"""
chunker = nltk.RegexpParser(grammar)

tok = nltk.regexp_tokenize(text, sentence_re)

postoks = nltk.tag.pos_tag(tok)

#print (postoks)

tree = chunker.parse(postoks)

from nltk.corpus import stopwords
stopwords = stopwords.words('english')


def leaves(tree):
    """Finds NP (nounphrase) leaf nodes of a chunk tree."""
    for subtree in tree.subtrees(filter = lambda t: t.label()=='NP'):
        yield subtree.leaves()

def normalise(word):
    """Normalises words to lowercase and stems and lemmatizes it."""
    word = word.lower()
    word = stemmer.stem_word(word)
    word = lemmatizer.lemmatize(word)
    return word

def acceptable_word(word):
    """Checks conditions for acceptable word: length, stopword."""
    accepted = bool(2 <= len(word) <= 40
        and word.lower() not in stopwords)
    return accepted


def get_terms(tree):
    for leaf in leaves(tree):
        term = [ normalise(w) for w,t in leaf if acceptable_word(w) ]
        yield term

terms = get_terms(tree)


with open("results.txt", "w+") as logfile:
    for term in terms: 
        for word in term:
            result = word
            logfile.write("%s\n" % str(word))
#           print (word),
#       (print)

logfile.close() 

Ramtin M. Se.. 5

另一种简便的方法是更改​​此部分：

tok = nltk.regexp_tokenize(text, sentence_re)
postoks = nltk.tag.pos_tag(tok)

并将其替换为nltk标准单词标记器：

toks = nltk.word_tokenize(text)
postoks = nltk.tag.pos_tag(toks)

从理论上讲，性能和结果之间应该没有太大差异。

另一种简便的方法是更改​​此部分：

tok = nltk.regexp_tokenize(text, sentence_re)
postoks = nltk.tag.pos_tag(tok)

并将其替换为nltk标准单词标记器：

toks = nltk.word_tokenize(text)
postoks = nltk.tag.pos_tag(toks)

从理论上讲，性能和结果之间应该没有太大差异。