NLP Lemmatisation（词性还原）和 Stemming（词干提取） NLTK pos_tag word_tokenize

亦凉 2021-06-11 15:11 869阅读 0赞

词形还原（lemmatization），是把一个词汇还原为一般形式（能表达完整语义），方法较为复杂；而词干提取（stemming）是抽取词的词干或词根形式（不一定能够表达完整语义），方法较为简单。  
**Stemming（词干提取）：**  
基于语言的规则。如英语中名词变复数形式规则。由于基于规则，可能出现规则外的情况。

# Porter Stemmer基于Porter词干提取算法
    from nltk.stem.porter import PorterStemmer  
    porter_stemmer = PorterStemmer()  
    porter_stemmer.stem('leaves')

# 输出：'leav'
    # 但实际应该是名词'leaf'

nltk中主要有以下方法：

# 基于Porter词干提取算法
    from nltk.stem.porter import PorterStemmer  
    porter_stemmer = PorterStemmer()  
    porter_stemmer.stem(‘maximum’)
    
    # 基于Lancaster 词干提取算法
    from nltk.stem.lancaster import LancasterStemmer  
    lancaster_stemmer = LancasterStemmer()  
    lancaster_stemmer.stem(‘maximum’)
    
    # 基于Snowball 词干提取算法
    from nltk.stem import SnowballStemmer  
    snowball_stemmer = SnowballStemmer(“english”)  
    snowball_stemmer.stem(‘maximum’)

**Lemmatisation（词性还原）：**  
基于字典的映射。nltk中要求手动注明词性，否则可能会有问题。因此一般先要分词、词性标注，再词性还原。

from nltk.stem import WordNetLemmatizer  
    lemmatizer = WordNetLemmatizer()  
    lemmatizer.lemmatize('leaves')

# 输出：'leaf'

完整过程：

word_tokenize("apples % , I've loves green")

![这里写图片描述][SouthEast]

pos_tag(word_tokenize("apples % , I've loves green"))

![这里写图片描述][SouthEast 1]

wnl = WordNetLemmatizer()
    wnl.lemmatize('apples', pos='n')

![这里写图片描述][SouthEast 2]

def lemmatize_all(sentence):
        wnl = WordNetLemmatizer()
        for word, tag in pos_tag(word_tokenize(sentence)):
            if tag.startswith('NN'):
                yield wnl.lemmatize(word, pos='n')
            elif tag.startswith('VB'):
                yield wnl.lemmatize(word, pos='v')
            elif tag.startswith('JJ'):
                yield wnl.lemmatize(word, pos='a')
            elif tag.startswith('R'):
                yield wnl.lemmatize(word, pos='r')
            else:
                yield word
    
    train_f = []
    test_f = []
    for i in range(0, len(train_feature)):
        train_f.append(' '.join(lemmatize_all(train_feature[i])))
    for i in range(0, len(test_feature)):
        test_f.append(' '.join(lemmatize_all(test_train[i])))

**NLTK词性：**

CC 连词 and, or,but, if, while,although
    CD 数词 twenty-four, fourth, 1991,14:24
    DT 限定词 the, a, some, most,every, no
    EX 存在量词 there, there's
    FW 外来词 dolce, ersatz, esprit, quo,maitre
    IN 介词连词 on, of,at, with,by,into, under
    JJ 形容词 new,good, high, special, big, local
    JJR 比较级词语 bleaker braver breezier briefer brighter brisker
    JJS 最高级词语 calmest cheapest choicest classiest cleanest clearest
    LS 标记 A A. B B. C C. D E F First G H I J K
    MD 情态动词 can cannot could couldn't
    NN 名词 year,home, costs, time, education
    NNS 名词复数 undergraduates scotches
    NNP 专有名词 Alison,Africa,April,Washington
    NNPS 专有名词复数 Americans Americas Amharas Amityvilles
    PDT 前限定词 all both half many
    POS 所有格标记 ' 's
    PRP 人称代词 hers herself him himself hisself
    PRP$ 所有格 her his mine my our ours
    RB 副词 occasionally unabatingly maddeningly
    RBR 副词比较级 further gloomier grander
    RBS 副词最高级 best biggest bluntest earliest
    RP 虚词 aboard about across along apart
    SYM 符号 % & ' '' ''. ) )
    TO 词to to
    UH 感叹词 Goodbye Goody Gosh Wow
    VB 动词 ask assemble assess
    VBD 动词过去式 dipped pleaded swiped
    VBG 动词现在分词 telegraphing stirring focusing
    VBN 动词过去分词 multihulled dilapidated aerosolized
    VBP 动词现在式非第三人称时态 predominate wrap resort sue
    VBZ 动词现在式第三人称时态 bases reconstructs marks
    WDT Wh限定词 who,which,when,what,where,how
    WP WH代词 that what whatever
    WP$ WH代词所有格 whose
    WRB WH副词

# 查看说明
    nltk.help.upenn_tagset(‘JJ’)

[SouthEast]: /images/20210520/a27c3037cb534b64af6ce0f50f3159ae.png
[SouthEast 1]: /images/20210520/c618e33d306245d4940901900ba06227.png
[SouthEast 2]: /images/20210520/2d3c45aeadb940f79fc3b4631d75e922.png