NLTK中英文分词下载配置与实例学习_nltk punkt下载

根据最近jieba分词的使用情况，对于英文句子的分词方式，整体感觉不如NLTK，因此通过本文具体记录NLTK如何安装，配置和基本的案例学习，实现了分词、词性标注、NER、句法分析、用户自定义分词方式等功能，供参考；同时由于nltk_data资源太大，下载速度太慢，这里也提供给大家，供研究使用。

1.安装NLTK，命令：pip install nltk

2.下载：直接从这里下载nltk_data资源，地址：https://download.csdn.net/download/hhue2007/86912857

3.解压：解压到当前文件夹后，将nltk_data拷贝到nltk能够识别到的任意一个路径下。

4.nltk能够识别的路径查找方法如下：

import nltk

print(nltk.find('.'))

5.进入nltk_data\tokenizers目录，解压punkt到当前文件夹

6.开始正常使用。NLTK的典型示例如下：

#coding:utf8
"""
Description:nltk分词操作详解
Author：hh by 2022-10-31
Prompt: code in Python3 env
Install：pip install nltk
""" 
import nltk
from nltk.tokenize import MWETokenizer
from nltk.corpus import stopwords
from nltk.tokenize.punkt import PunktSentenceTokenizer, PunktParameters
# print(nltk.find('.')) 
def cut_sentences_en(content):
    punkt_param = PunktParameters()
    abbreviation = ['i.e.', 'dr', 'vs', 'mr', 'mrs', 'prof', 'inc','99/22','North China Sea']  # 自定义的词典
    punkt_param.abbrev_types = set(abbreviation)
    tokenizer = PunktSentenceTokenizer(punkt_param)
    tokenizer._params.abbrev_types
    sentences = tokenizer.tokenize(content)
    return sentences
if __name__=='__main__':
    #********************1 nltk中文分词基本操作***********************************
    print('='*40)
    str="Geological Final Well Report Well LH35-13-1 Block 99/22,我来自北京大学"
    token_list=nltk.word_tokenize(str)
    print("\n1.分词: ", "$ ".join(token_list))
 
    taged_list=nltk.pos_tag(token_list)
    print("\n2.词性标注: ", taged_list)
 
    #NER需要利用词性标注的结果
    ners = nltk.ne_chunk(taged_list)
    print("\n3.实体识别(NER): ", ners)
 
    entity_list=nltk.chunk.ne_chunk(taged_list)
    print("\n4.句法分析: ", entity_list)
 
    str2='Geological Final Well Report Well LH35-13-1 Block 99/22, North China Sea,LH35-13-1 GEOLOGICAL COMPOSITE LOG,Geological Final Well Report'
    tokenized_string = nltk.word_tokenize(str2)
    mwe = [('Geological','Final','Well','Report','Well'),('99/','22'),('North','China','Sea'),('GEOLOGICAL','COMPOSITE','LOG')] # 添加这个短语(phrase)
    mwe_tokenizer = nltk.tokenize.MWETokenizer(mwe)
    result = mwe_tokenizer.tokenize(tokenized_string)
    print("\n5.用户自定义分词: " + "/ ".join(result))
    print('\n6.用户自定义分词(简称）：',cut_sentences_en(str2))