自然语言处理

1.处理框架

Ltp:中文分词、分词标注、未登陆词识别、句法分析、语义角色标注

Stanford NLP:中文分词、分词标注、未登陆词识别、句法分析

FudanNLP:中文分词、句法分析

HanLP:中文分词、句法分析等各类算法

 

ICTCLAS分词系统:具有里程碑意义的中文分词系统

Anjs中文分词系统:中等规模的分词系统

jieba:小规模中文分词

 

2.分词功能

(1).ltp分词

import sys
import os
from pyltp import Segmentor


seg = Segmentor()
seg.load("modal path")
words = seg.segment('大家好我是一个例子')

print(words)

(2).jieba分词

import sys
import os
import jieba

#全模式
wordlist = jieba.cut("大家好我是一个例子",cut_all=True)
print("|".join(wordlist))

#精确模式
wordlist = jieba.cut("大家好我是一个例子")
print("|".join(wordlist))

#搜索模式
wordlist = jieba.cut_for_search("大家好我是一个例子")
print("|".join(wordlist))

 

3.词性标注

import sys
import os
from pyltp import *

words = ['我','是','一个','人']
pst = Postagger()

pst.load('model path')
tags = pst.postag(words)

for word,tag in zip(words,tags):
    print(word + '-' + tag)

 

4.实体识别

rcz = NamedEntityRecognizer()
rez.load('model path')

ntags = rez.recognize(words,tags)

print(ntags)

 

5.句法分析

import nltk
from nltk.tree import Tree
from nltk.grammar import DependencyGrammarp
from nltk.parse import *


parser = Parser()
parser.load('model path')
arcs = parser.parse(words,tags)

coll = ''

for i in xrange(len(arcs)):
    if arcs[i].head == 0:
        arcs[i].relation = 'ROOT'
    coll += '\t' + words[i] + '(' + tags[i] + ')' + '\t' + tags[i] + '\t' + arcs[i].head + '\t' + arcs[i].relation
    print(coll)

ctree = DependencyGraph(coll)
ctree.tree().draw()

 

来源：https://www.cnblogs.com/yangyang12138/p/12417889.html