NDLI: UNSUPERVISED COMBINATION OF METRICS FOR SEMANTIC CLASS INDUCTION

Content Provider	IEEE Xplore Digital Library
Author	Iosif, E. Tegos, A. Pangos, A. Fosler-Lussier, E. Potamianos, A.
Copyright Year	2006
Description	Author affiliation: Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania (Iosif, E.; Tegos, A.; Pangos, A.)
Abstract	In this paper, unsupervised algorithms for combining semantic similarity metrics are proposed for the problem of automatic class induction. The automatic class induction algorithm is based on the work of Pargellis et al,. The semantic similarity metrics that are evaluated and combined are based on narrow- and wide-context vector- product similarity. The metrics are combined using linear weights that are computed 'on the fly' and are updated at each iteration of the class induction algorithm, forming a corpus-independent metric. Specifically, the weight of each metric is selected to be inversely proportional to the inter-class similarity of the classes induced by that metric and for the current iteration of the algorithm. The proposed algorithms are evaluated on two corpora: a semantically heterogeneous news domain (HR-Net) and an application-specific travel reservation corpus (ATIS). It is shown, that the (unsupervised) adaptive weighting scheme outperforms the (supervised) fixed weighting scheme. Up to 50% relative error reduction is achieved by the adaptive weighting scheme.
Starting Page	86
Ending Page	89
File Size	316287
Page Count	4
File Format	PDF
ISBN	1424408725
DOI	10.1109/SLT.2006.326823
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-10
Publisher Place	Aruba
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Vocabulary Induction generators Natural languages Ontologies Information retrieval Speech Iterative algorithms Data mining Text processing
Content Type	Text
Resource Type	Article

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