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  • 02 NLTK 分句、分词、词干提取、词型还原

    NLTK 分句、分词、词干提取、词型还原

    print("==========案例1:分句、分词===============")
    import nltk.tokenize as tk
    doc ="Are you curious about tokenization? ""Let's see how it works! "
    "We need to analyze a couple of sentences " "with punctuations to see it in action."
    
    print(doc)
    
    # 按句拆分:tk.sent_tokenize(doc)
    # 问:tk.sent_tokenize()为何能识别出到哪里是一句?
    # 答:1、看首字母是大写 ;2、结尾有标点符号
    tokens = tk.sent_tokenize(doc)
    for i,token in enumerate(tokens):
        print("%2d" % (i+1),token)
    
    print("-----------------------------")
    
    # 按词拆分:tk.word_tokenize(doc)
    tokens = tk.word_tokenize(doc)
    for i,token in enumerate(tokens):
        print("%2d" % (i+1),token)
    
    
    # 按词和标点拆分:tk.WordPunctTokenizer().tokenize(doc)
    tokenizer=tk.WordPunctTokenizer()
    tokens = tokenizer.tokenize(doc)
    for i,token in enumerate(tokens):
        print("%2d" % (i+1),token)
        
    print("=============案例2:词干提取、词型还原===================")    
    
    # 导入下面三种词干提取器进行对比
    import nltk.stem.porter as pt
    import nltk.stem.lancaster as lc
    import nltk.stem.snowball as sb
    
    # 导入nltk.stem用来词型还原
    import nltk.stem as ns
    
    
    words = ['table', 'probably', 'wolves', 'playing',
             'is', 'dog', 'the', 'beaches', 'grounded',
             'dreamt', 'envision']
    print(words)
    
    print("----------词干提取-------------")
    # 在名词和动词中,除了与数和时态有关的成分以外的核心成分。
    # 词干并不一定是合法的单词
    
    pt_stemmer = pt.PorterStemmer()  # 波特词干提取器
    lc_stemmer = lc.LancasterStemmer()   # 兰卡斯词干提取器
    sb_stemmer = sb.SnowballStemmer("english")# 思诺博词干提取器
    
    for word in words:
        pt_stem = pt_stemmer.stem(word)
        lc_stem = lc_stemmer.stem(word)
        sb_stem = sb_stemmer.stem(word)
        print("%8s %8s %8s %8s" % (word,pt_stem,lc_stem,sb_stem))
    
    
    print("----------词型还原器---------------")
    # 词型还原:复数名词->单数名词 ;分词->动词原型
    # 单词原型一定是合法的单词
    
    lemmatizer = ns.WordNetLemmatizer()
    for word in words:
        # 将名词还原为单数形式
        n_lemma = lemmatizer.lemmatize(word, pos='n')
        # 将动词还原为原型形式
        v_lemma = lemmatizer.lemmatize(word, pos='v')
        print('%8s %8s %8s' % (word, n_lemma, v_lemma))
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  • 原文地址:https://www.cnblogs.com/wodexk/p/10292947.html
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