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一、准备工作

①安装好nltk模块并在：

nltk/nltk_data: NLTK Data

链接中手动下载模型并放入到对应文件夹下。

具体放到哪个文件夹，先执行看报错后的提示即可。

②准备pos_map.json文件，放置到当前文件夹下。该文件用于词性统一

{"NN": "n","NNS": "n","NNP": "n","NNPS": "n","PRP": "n","PRP$": "n","VB": "v","VBD": "v","VBG": "v","VBN": "v","VBP": "v","VBZ": "v","MD": "v","JJ": "a","JJR": "s","JJS": "s","RB": "r","RBR": "r","RBS": "r","IN": "r","TO": "r","CD": "n","DT": "a","WDT": "a","CC": "r","UH": "r"
}

二、执行下述代码

from nltk import word_tokenize, pos_tag
from nltk.stem import WordNetLemmatizer
import jsondef tokenize_and_tag(sentence):# 分词tokens = word_tokenize(sentence)# 词性标注tagged = pos_tag(tokens)# 分离单词和标签words = [item[0] for item in tagged]pos_tags = [item[1] for item in tagged]return words, pos_tags# 示例用法
wnl = WordNetLemmatizer()
sentence = "The quick brown fox jumps over the lazy dog."
words, pos_tags = tokenize_and_tag(sentence)print("分词列表:", words)
print("词性列表:", pos_tags)with open("pos_map.json", "r", encoding="utf-8") as f:pos_map: dict = json.load(f)pos_tags = [pos_map.get(tag, "n") for tag in pos_tags]for i in range(len(words)):print(words[i]+'--'+pos_tags[i]+'-->'+wnl.lemmatize(words[i],pos_tags[i]))

示例结果：

分词列表: ['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog', '.']
词性列表: ['DT', 'JJ', 'NN', 'NN', 'VBZ', 'IN', 'DT', 'JJ', 'NN', '.']
The--a-->The
quick--a-->quick
brown--n-->brown
fox--n-->fox
jumps--v-->jump
over--r-->over
the--a-->the
lazy--a-->lazy
dog--n-->dog
.--n-->.