Python文本处理：分词与词性标注

使用Python编写程序进行文本分词与词性标注

本文将介绍如何使用Python进行文本分词和词性标注，并提供英文和中文文本的示例。

英文文本分词

代码：

import nltk

text = 'Big data is data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are a number of concepts associated with big data: originally there were 3 concepts volume, variety and velocity. Other concepts later attributed with big data are veracity and value.'
sentences = nltk.sent_tokenize(text)

print(sentences)

输出：

['Big data is data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them.', 'Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.', 'There are a number of concepts associated with big data: originally there were 3 concepts volume, variety and velocity.', 'Other concepts later attributed with big data are veracity and value.']

代码：

import nltk

text = 'Big data is data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are a number of concepts associated with big data: originally there were 3 concepts volume, variety and velocity. Other concepts later attributed with big data are veracity and value.'
words = nltk.word_tokenize(text)

print(words)

输出：

['Big', 'data', 'is', 'data', 'sets', 'that', 'are', 'so', 'big', 'and', 'complex', 'that', 'traditional', 'data-processing', 'application', 'software', 'are', 'inadequate', 'to', 'deal', 'with', 'them', '.', 'Big', 'data', 'challenges', 'include', 'capturing', 'data', ',', 'data', 'storage', ',', 'data', 'analysis', ',', 'search', ',', 'sharing', ',', 'transfer', ',', 'visualization', ',', 'querying', ',', 'updating', ',', 'information', 'privacy', 'and', 'data', 'source', '.', 'There', 'are', 'a', 'number', 'of', 'concepts', 'associated', 'with', 'big', 'data', ':', 'originally', 'there', 'were', '3', 'concepts', 'volume', ',', 'variety', 'and', 'velocity', '.', 'Other', 'concepts', 'later', 'attributed', 'with', 'big', 'data', 'are', 'veracity', 'and', 'value', '.']

中文文本分词与词性标注

代码：

import jieba.posseg as pseg

text = '大数据是需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。'

words = pseg.cut(text)

for word, flag in words:
    print(word, flag)

输出：

大数据 n
是 v
需要 v
新 a
处理 v
模式 n
才能 c
具有 v
更强 a
的 uj
决策力 n
、 x
洞察发现力 n
和 c
流程优化能力 n
的 uj
海量 n
、 x
高 a
增长率 n
和 c
多样化 a
的 uj
信息资产 n
。 x

总结

本文介绍了如何使用Python进行英文和中文文本的分词和词性标注。这些基本操作是自然语言处理中重要的第一步，可以用于后续的文本分析、机器学习等任务。