Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40715
Title: Extracting key-substring-group features for text classification
Authors: Zhang, D.
Lee, W.S. 
Keywords: Feature Extraction
Machine Learning
Suffix Tree
Text Classification
Text Mining
Issue Date: 2006
Source: Zhang, D.,Lee, W.S. (2006). Extracting key-substring-group features for text classification. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006 : 474-483. ScholarBank@NUS Repository.
Abstract: In many text classification applications, it is appealing to take every document as a string of characters rather than a bag of words. Previous research studies in this area mostly focused on different variants of generative Markov chain models. Although discriminative machine learning methods like Support Vector Machine (SVM) have been quite successful in text classification with word features, it is neither effective nor efficient to apply them straightforwardly taking all substrings in the corpus as features. In this paper, we propose to partition all substrings into statistical equivalence groups, and then pick those groups which are important (in the statistical sense) as features (named key-substring-group features) for text classification. In particular, we propose a suffix tree based algorithm that can extract such features in linear time (with respect to the total number of characters in the corpus). Our experiments on English, Chinese and Greek datasets show that SVM with key-substring-group features can achieve outstanding performance for various text classification tasks. Copyright 2006 ACM.
Source Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
URI: http://scholarbank.nus.edu.sg/handle/10635/40715
ISBN: 1595933395
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

50
checked on Dec 9, 2017

Google ScholarTM

Check


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