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Unsupervised Cue-words Discovery for Tag-sense Disambiguation: Comparing Dissimilarity Metrics

Legesse, Meshesha and Gianini, Gabriele and Teferi, Dereje and Mousselly-Sergieh, Hatem and Coquil, David and Egyed-Zsigmond, Elöd (2015):
Unsupervised Cue-words Discovery for Tag-sense Disambiguation: Comparing Dissimilarity Metrics.
In: Proceedings of the 7th International Conference on Management of Computational and Collective intElligence in Digital EcoSystems, ACM, Caraguatatuba, Brazil, ISBN 978-1-4503-3480-8,
DOI: 10.1145/2857218.2857222,
[Online-Edition: https://dl.acm.org/citation.cfm?id=2857222&dl=ACM&coll=DL],
[Conference or Workshop Item]

Abstract

Although tagging simplifies resource browsing and retrieval, it suffers from several issues: among them are redundancy and ambiguity. In this work we focus on the problem of resolving tag word-sense ambiguity within a typical semi-automatic tagging procedure. In that process a user proposes a tag for a resource, if the tag is found to be related to more than one context, she is provided with two or more cues among which to choose, so as to remove the tag ambiguity. Key phases, in such a disambiguation procedure, are ambiguous tag detection and cue discovery. Both should rely on effective word-to-context relatedness metrics. Among the most effective relatedness metrics are those defined on the basis of a feature vector representation of the words. In this work we compare different word-to-context relatedness metrics in terms of effectiveness within the disambiguation process. We propose to use a metrics derived from a Maximum Likelihood estimator of the Jensen-Shannon Divergence among feature-count histograms and we show that such a metrics performs -- in terms of quality of the output -- better than both the Jensen-Shannon and the Symmetrized Kullback-Leibler divergence between histograms. We study the relative gain in quality within the task of unsupervised cue discovery by using a synthetic language corpus.

Item Type: Conference or Workshop Item
Erschienen: 2015
Creators: Legesse, Meshesha and Gianini, Gabriele and Teferi, Dereje and Mousselly-Sergieh, Hatem and Coquil, David and Egyed-Zsigmond, Elöd
Title: Unsupervised Cue-words Discovery for Tag-sense Disambiguation: Comparing Dissimilarity Metrics
Language: English
Abstract:

Although tagging simplifies resource browsing and retrieval, it suffers from several issues: among them are redundancy and ambiguity. In this work we focus on the problem of resolving tag word-sense ambiguity within a typical semi-automatic tagging procedure. In that process a user proposes a tag for a resource, if the tag is found to be related to more than one context, she is provided with two or more cues among which to choose, so as to remove the tag ambiguity. Key phases, in such a disambiguation procedure, are ambiguous tag detection and cue discovery. Both should rely on effective word-to-context relatedness metrics. Among the most effective relatedness metrics are those defined on the basis of a feature vector representation of the words. In this work we compare different word-to-context relatedness metrics in terms of effectiveness within the disambiguation process. We propose to use a metrics derived from a Maximum Likelihood estimator of the Jensen-Shannon Divergence among feature-count histograms and we show that such a metrics performs -- in terms of quality of the output -- better than both the Jensen-Shannon and the Symmetrized Kullback-Leibler divergence between histograms. We study the relative gain in quality within the task of unsupervised cue discovery by using a synthetic language corpus.

Title of Book: Proceedings of the 7th International Conference on Management of Computational and Collective intElligence in Digital EcoSystems
Publisher: ACM
ISBN: 978-1-4503-3480-8
Uncontrolled Keywords: Jensen-Shannon divergence, disambiguation, dissimilarity metrics, retrieval models and ranking, semantic relatedness, similarity measures, tagging
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Event Location: Caraguatatuba, Brazil
Date Deposited: 31 Dec 2016 14:29
DOI: 10.1145/2857218.2857222
Official URL: https://dl.acm.org/citation.cfm?id=2857222&dl=ACM&coll=DL
Identification Number: TUD-CS-2015-12061
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