Hierarchical Bayesian Clustering for Automatic Text Classification
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Natural Language. Pages 1322-1326.
Abstract
Text classification, the grouping of texts into several clusters, has been used as a means of improving both the efficiency and the effectiveness of text retrieval/categorization. In this paper we propose a hierarchical clustering algorithm that constructs a set of clusters having the maximum Bayesian posterior probability, the probability that the given texts are classified into clusters. We call the algorithm Hierarchical Bayesian Clustering (HBC). The advantages of HBC are experimentally verified from several viewpoints. (1) HBC can re-construct the original clusters more accurately than do other non probabilistic algorithms. (2) When a probabilistic text categorization is extended to a cluster-based one, the use of HBC offers better performance than does the use of non probabilistic algorithms.
@inproceedings{ijcai1995p170,
title = {Hierarchical Bayesian Clustering for Automatic Text Classification},
author = {Iwayama, Makoto and Tokunaga, Takenobu},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1322--1326},
year = {1995},
note = {Natural Language},
url = {https://ijcai.org/proceedings/1995/170},
}
TY - CPAPER TI - Hierarchical Bayesian Clustering for Automatic Text Classification AU - Iwayama, Makoto AU - Tokunaga, Takenobu T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1322 EP - 1326 UR - https://ijcai.org/proceedings/1995/170 ER -Copied
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