中国科学院软件研究所机构知识库
Advanced  
ISCAS OpenIR  > 软件所图书馆  > 期刊论文
Title:
word combination kernel for text categorization
Author: Zhang Lujiang ; Hu Xiaohui ; Qin Shiyin
Keyword: Algorithms ; Learning systems ; Support vector machines
Source: Journal of Digital Information Management
Issued Date: 2012
Volume: 10, Issue:3, Pages:202-211
Indexed Type: EI
Department: (1) School of Automation Science and Electrical Engineering Beijing University of Aeronautics and Astronautics Beijing 100191 China; (2) Institute of Software Chinese Academy of Sciences Beijing 100190 China
Abstract: We proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.
English Abstract: We proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.
Language: 英语
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/15034
Appears in Collections:软件所图书馆_期刊论文

Files in This Item:

There are no files associated with this item.


Recommended Citation:
Zhang Lujiang,Hu Xiaohui,Qin Shiyin. word combination kernel for text categorization[J]. Journal of Digital Information Management,2012-01-01,10(3):202-211.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Zhang Lujiang]'s Articles
[Hu Xiaohui]'s Articles
[Qin Shiyin]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Zhang Lujiang]‘s Articles
[Hu Xiaohui]‘s Articles
[Qin Shiyin]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2019  中国科学院软件研究所 - Feedback
Powered by CSpace