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Authors: Shashank Paliwal and Vikram Pudi

Affiliation: International Institute of Information Technology Hyderabad, India

Keyword(s): Text document clustering, Document similarity, Term proximity, Term dependency, Feature weighting.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining Text and Semi-Structured Data ; Symbolic Systems

Abstract: Measuring inter-document similarity is one of the most essential steps in text document clustering. Traditional methods rely on representing text documents using the simple Bag-of-Words (BOW) model which assumes that terms of a text document are independent of each other. Such single term analysis of the text completely ignores the underlying (semantic) structure of a document. In the literature, sufficient efforts have been made to enrich BOW representation using phrases and n-grams like bi-grams and tri-grams. These approaches take into account dependency only between adjacent terms or a continuous sequence of terms. However, while some of the dependencies exist between adjacent words, others are more distant. In this paper, we make an effort to enrich traditional document vector by adding the notion of term-pair features. A Term-Pair feature is a pair of two terms of the same document such that they may be adjacent to each other or distant. We investigate the process of term-pair selection and propose a methodology to select potential term-pairs from the given document. Utilizing term proximity between distant terms also allows some flexibility for two documents to be similar if they are about similar topics but with varied writing styles. Experimental results on standard web document data set show that the clustering performance is substantially improved by adding term-pair features. (More)

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Paper citation in several formats:
Paliwal, S. and Pudi, V. (2011). UTILIZING TERM PROXIMITY BASED FEATURES TO IMPROVE TEXT DOCUMENT CLUSTERING. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - SSTM, (IC3K 2011) ISBN 978-989-8425-79-9; ISSN 2184-3228, pages 529-536. DOI: 10.5220/0003645805370544

@conference{sstm11,
author={Shashank Paliwal. and Vikram Pudi.},
title={UTILIZING TERM PROXIMITY BASED FEATURES TO IMPROVE TEXT DOCUMENT CLUSTERING},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - SSTM, (IC3K 2011)},
year={2011},
pages={529-536},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003645805370544},
isbn={978-989-8425-79-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - SSTM, (IC3K 2011)
TI - UTILIZING TERM PROXIMITY BASED FEATURES TO IMPROVE TEXT DOCUMENT CLUSTERING
SN - 978-989-8425-79-9
IS - 2184-3228
AU - Paliwal, S.
AU - Pudi, V.
PY - 2011
SP - 529
EP - 536
DO - 10.5220/0003645805370544