@inproceedings{590e3fa3a46241e2ac18e6185f1c501a,
title = "Semantic pattern tree kernels for short-text classification",
abstract = "Kernel methods are widely used for document classification in diverse domains. Popular kernels such as bag-of-word kernels and tree kernels show satisfactory results in classifying documents such as articles, e-mails or web pages. However, they provide less satisfactory performances in classifying short-text documents since the short documents have insufficient feature space. In order to cope with the problem, this paper presents a novel kernel function called semantic pattern tree kernel for classifying short-text documents. The proposed kernel extends the feature space of each document by incorporating syntactic and semantic information using three levels of semantic annotations. Experiments on the Open Directory Project dataset show that in classifying short-text documents the semantic pattern tree kernels achieve higher accuracy than the conventional kernels.",
keywords = "document classification, kernel methods, open directory project, semantic, short-text document, support vector machine",
author = "Kwanho Kim and Chung, {Beom Suk} and Choi, {Ye Rim} and Jonghun Park",
year = "2011",
doi = "10.1109/DASC.2011.202",
language = "English",
isbn = "9780769546124",
series = "Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011",
pages = "1250--1252",
booktitle = "Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011",
note = "9th IEEE Int. Conf. on Dependable, Autonomic and Secure Comput., DASC 2011, incl. 9th Int. Conf. on Pervasive Intelligence and Computing, PICom 2011, 9th Int. Symp. on Embedded Computing, EmbeddedCom 2011, 1st Int. Conf. on Cloud and Green Comput.CGC ; Conference date: 12-12-2011 Through 14-12-2011",
}