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浙江大学学报(工学版)
J4  2009, Vol. 43 Issue (6): 994-998    DOI: 10.3785/j.issn.1008-973X.2009.
计算机技术、自动化技术     
利用加权特征模型改进问句分类
黄鹏1,卜佳俊1,陈纯1,康志明1,陈伟1,胡洪涛2
(1浙江大学 计算机科学与技术学院,浙江 杭州 310027；2浙江丽水电业局,浙江 丽水 323000)
Improving question classification via weighted feature model
HUANG Peng1, BU Jia-jun1, CHEN Chun1, KANG Zhi-ming1, CHEN Wei1, HU Hong-tao2
（1. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;
2. Zhejiang Lishui Electric Power Bureau, Lishui 323000, China）
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摘要：

为了减少目前大多数问句分类算法由于采用了布尔特征模型所导致的特征信息损失,提出了一个基于网络和聚类技术的加权特征模型来表达问句的特征空间.不同于以往采用的布尔特征模型将特征赋值为0或1以表示相应特征出现与否,新的加权特征模型将特征加权为一个位于区间0～10的一个实数,以区分不同的特征对于问句分类的贡献：权值越大,相应特征对于区分问句的类型做出的贡献越大.试验结果表明,该加权特征模型在问句分类领域优于之前被广泛使用的布尔特征模型.
Abstract:

A novel feature weighting model based on internet and clustering techniques was proposed to mitigate the issue of feature information loss stemming from the binary feature model employed in most current question classification algorithms, in which features are associated with weights in a range of 0～10, rather than simple binary values in the binary feature model. These weights can be used to quantify features contribution to question classification. Features with larger weights should be more contributable to the question classification. The experimental results show that the proposed feature weighting model outperforms the widely used binary feature model in the task of question classification．
出版日期: 2009-06-01
:  TP391  
通讯作者: 卜佳俊，男，教授.     E-mail: bjj@zju.edu.cn
作者简介: 黄鹏（1979-），男，江西宜春人，博士生，从事信息检索和自然语言处理方面的研究.
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引用本文:

黄鹏, 卜佳俊, 陈纯, 等. 利用加权特征模型改进问句分类[J]. J4, 2009, 43(6): 994-998.

HUANG Feng, BO Jia-Dun, CHEN Chun, et al. Improving question classification via weighted feature model. J4, 2009, 43(6): 994-998.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2009.    或    http://www.zjujournals.com/eng/CN/Y2009/V43/I6/994

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