Consensus Similarity Measure for Short Text Clustering

Consensus Similarity Measure for Short Text Clustering

Measuring semantic similarity between short texts is challenging because the meaning of short texts may vary dramatically even by a few words due to their limited lengths. In this paper, we propose a novel similarity measure for terms that allows better clustering performance than the state-of-the-a...

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Bibliographic Details
Published in:2015 26th International Workshop on Database and Expert Systems Applications (DEXA) pp. 264 - 268
Main Authors: Shin, Youhyun, Ahn, Yeonchan, Jeon, Heesik, Lee, Sang-goo
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01-09-2015
Subjects:
Batteries
Clustering
Context
Expert systems
Knowledge base
Knowledge based systems
Length measurement
Natural language processing
semantic similarity
Semantics
short text
Similarity
State of the art
Taxonomy
Texts
Workshops
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Summary:Measuring semantic similarity between short texts is challenging because the meaning of short texts may vary dramatically even by a few words due to their limited lengths. In this paper, we propose a novel similarity measure for terms that allows better clustering performance than the state-of-the-art method. To achieve such performance, we incorporate knowledge-based and corpus-based term similarity measures in order to exploit advantages of both approaches. We apply our method to a dialog-utterance dataset, which consists of short dialog texts. Empirical study shows that the proposed method outperforms one of the state-of-the-art clustering algorithms for short text clustering.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISBN:1467375810
9781467375818
ISSN:1529-4188
2378-3915
DOI:10.1109/DEXA.2015.65