Python Chinese participle: stuttering participle

Chinese word segmentation is a basic work in Chinese text processing, and stuttering participle is used for Chinese word segmentation. Its basic implementation principle has three points:

1. Efficient word-map scanning based on trie tree structure to generate a directed acyclic graph (DAG) consisting of all possible words of Chinese characters in a sentence
2. Using dynamic programming to find the maximum probability path, the maximum segmentation combination based on word frequency is found.
3. For the non-login words, the hmm model based on Chinese characters ' lexical ability is adopted, and the Viterbi algorithm is used.

Installation (Linux environment)

Download the toolkit, unzip it into the directory, run: Python setup.py install

Mode

1. Default mode, try to cut the sentence most accurately, suitable for text analysis
2. Full mode, the sentence all can be the word words are scanned out, suitable for the search engine

Interface

• Component only provides Jieba.cut method for participle
• The Cut method accepts two input parameters:
  • The first argument is a string that requires a word breaker
  • Cut_all parameter to control word breaker mode
• The string to be participle can be a GBK string, a utf-8 string, or a Unicode
• The structure returned by Jieba.cut is an iterative generator that can use a for loop to obtain every word (Unicode) that is obtained after a word breaker, or it can be used with list (Jieba.cut (...)). Convert to List

Instance

# !-*-coding:utf-8-*- Import  = jieba.cut (" I came to Beijing Tsinghua University ", Cut_all = True)print" Full Mode:"' = Jieba.cut (" I came to Tsinghua University in Beijing  ")print"Default Mode:"  '. Join (Seg_list)

Results

Implementation principle

1. Trie Tree: Reference http://www.cnblogs.com/kaituorensheng/p/3602155.html

Python Chinese participle: stuttering participle