Short-Text Clustering using Statistical Semantics Sepideh Seifzadeh Ahmed K. Farahat Mohamed S. Kamel University of Waterloo University of Waterloo University of Waterloo Waterloo, Ontario, Canada. Waterloo, Ontario, Canada. Waterloo, Ontario, Canada. N2L 3G1 N2L 3G1 N2L 3G1
[email protected] [email protected] [email protected] Fakhri Karray University of Waterloo Waterloo, Ontario, Canada. N2L 3G1
[email protected] ABSTRACT 1. INTRODUCTION Short documents are typically represented by very sparse In social media, users usually post short texts. Twitter vectors, in the space of terms. In this case, traditional limits the length of each Tweet to 140 characters; therefore, techniques for calculating text similarity results in measures developing data mining techniques to handle the large vol- which are very close to zero, since documents even the very ume of short texts has become an important goal [1]. Text similar ones have a very few or mostly no terms in common. document clustering has been widely used to organize doc- In order to alleviate this limitation, the representation of ument databases and discover similarity and topics among short-text segments should be enriched by incorporating in- documents. Short text clustering is more challenging than formation about correlation between terms. In other words, regular text clustering; due to the sparsity and noise, they if two short segments do not have any common words, but provide very few contextual clues for applying traditional terms from the first segment appear frequently with terms data mining techniques [2]; therefore, short documents re- from the second segment in other documents, this means quire different or more adapted approaches.