我是一个新的python，正在处理一个文本分类问题。我用不同的在线资源开发了一个代码。但是这个代码并没有做词性标注。有人能帮我找出我的代码中我真正出错的那一行吗。我在代码中做词性标记，但结果中没有显示。我也试过用nltk做词性标注，但这对我也不起作用。如有任何帮助，我们将不胜感激。谢谢。在# Add the Data using pandas

Corpus = pd.read_csv(r"U:\FAHAD UL HASSAN\Python Code\projectdatacor.csv",encoding='latin-1')

# Data Pre-processing - This will help in getting better results through the classification algorithms

# Remove blank rows if any.

Corpus['description'].dropna(inplace=True)

# Change all the text to lower case. This is required as python interprets 'design' and 'DESIGN' differently

Corpus['description'] = [entry.lower() for entry in Corpus['description']]

# Punctuation Removal

Corpus['description'] = Corpus.description.str.replace('[^\w\s]', '')

# Tokenization : In this each entry in the corpus will be broken into set of words

Corpus['description']= [word_tokenize(entry) for entry in Corpus['description']]

# Remove Stop words, Non-Numeric and perfom Word Stemming/Lemmenting.

# WordNetLemmatizer requires Pos tags to understand if the word is noun or verb or adjective etc. By default it is set to Noun

STOPWORDS = set(stopwords.words('english'))

tag_map = defaultdict(lambda : wn.NOUN)

tag_map['J'] = wn.ADJ

tag_map['V'] = wn.VERB

tag_map['R'] = wn.ADV

for index,entry in enumerate(Corpus['description']):

# Declaring Empty List to store the words that follow the rules for this step

Final_words = []

# Initializing WordNetLemmatizer()

word_Lemmatized = WordNetLemmatizer()

# pos_tag function below will provide the 'tag' i.e if the word is Noun(N) or Verb(V) or something else.

for word, tag in pos_tag(entry):

# Below condition is to check for Stop words and consider only alphabets

if word not in STOPWORDS and word.isalpha():

word_Final = word_Lemmatized.lemmatize(word,tag_map[tag[0]])

Final_words.append(word_Final)

# The final processed set of words for each iteration will be stored in 'description_final'

Corpus.loc[index,'description_final'] = str(Final_words)

print(Corpus['description_final'].head())

这些就是我得到的结果。这段代码做了很多事情，比如标记化，删除了stopwords，但是它在我的结果中显示了pos标记。在

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