我是一個新的python，正在處理一個文本分類問題。我用不同的在線資源開發(fā)了一個代碼。但是這個代碼并沒有做詞性標注。有人能幫我找出我的代碼中我真正出錯的那一行嗎。我在代碼中做詞性標記，但結果中沒有顯示。我也試過用nltk做詞性標注，但這對我也不起作用。如有任何幫助，我們將不勝感激。謝謝。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())

這些就是我得到的結果。這段代碼做了很多事情，比如標記化，刪除了stopwords，但是它在我的結果中顯示了pos標記。在

^{pr2}$

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