[1]刁琦[],古丽米拉·克孜尔别克[],钟丽峰[],等. 基于循环神经网络序列标注的中文分词研究[J].计算机技术与发展,2017,27(10):65-68.
　DIAO Qi[],Gulimila·KEZIERBIEKE[],Zhong Li-feng[],et al.Research on Chinese Word Segmentation Method of Sequence Labeling Based on Recurrent Neural Networks[J].,2017,27(10):65-68.
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 基于循环神经网络序列标注的中文分词研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年10期
页码:
65-68
栏目:
智能、算法、系统工程
出版日期:
2017-10-10

文章信息/Info

Title:
Research on Chinese Word Segmentation Method of Sequence Labeling Based on Recurrent Neural Networks
文章编号:
1673-629X（2017)10-0065-04
作者:
 刁琦[1]；古丽米拉·克孜尔别克[1]；钟丽峰[2]；张健[3]；张志强[1]
 1.新疆农业大学 计算机与信息工程学院；2.新疆维吾尔族自治区图书馆；3.新疆虹联软件有限公司
Author(s):
 DIAO Qi[1]；Gulimila·KEZIERBIEKE[1]；Zhong Li-feng[2]；ZHANG Jian[3]；ZHANG Zhi-qiang[1]
关键词:
 ; 自然语言处理; 循环神经网络; 序列标注; 中文分词; 监督学习
Keywords:
 ; natural language processing; recurrent neural network; sequence annotation; Chinese word segmentation;  supervised learning
分类号:
TP301.6
文献标志码:
A
摘要:
 分词是中文自然语言处理中的关键技术.在自然语言处理中,序列标注在中文分词中有着极其重要的应用.当前主流的中文分词方法是基于监督学习,从中文文本中提取特征信息.这些方法未能充分地利用上下文信息对中文进行分割,缺乏长距离信息约束能力.针对上述问题进行研究,提出在序列标注的前提下利用双向循环神经网络模型进行中文分词,避免了窗口对上下文大小的限制,可以获得一个词的前面和后面的上下文信息,通过增加上下文能够有效地解决梯度爆炸和爆的问题,然后再在输入层加入训练好的上下文词向量,取得相对较好的分词效果.实验结果表明,该算法的使用可以达到97.3%的中文分词准确率,与传统机器学习分词算法相比,效果较为显著.
Abstract:
 Word segmentation is a key technology in Chinese natural language processing. In natural language processing,sequence labe-ling plays an important role in Chinese word segmentation. The current mainstream Chinese word segmentation method is based on super-vised learning,extraction of feature information from the Chinese text. However,they cannot make full use of context information to seg-ment Chinese,and lack of long-distance information constraint. In order to solve it,Chinese word segmentation is carried on based on bi-directional recurrent neural network model on the premise of sequence labeling,avoiding the limitation of window size on context,obtai-ning the context information of the front and back of a word. It can effectively solve the problem of gradient explosion and explosion by adding context information,and then add a good context vector in the input layer to obtain a relatively good word segmentation effect. The experimental results show that it can achieve 97. 3% accuracy of Chinese word segmentation and is superior to the traditional ma-chine learning segmentation algorithm in the effect.

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更新日期/Last Update: 2017-11-23