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Highlighting relevant concepts from Topic Signatures
BibTeX
@MISC{Cuadros_highlightingrelevant,
author = {Montse Cuadros and German Rigau and Vicomtech-ik Donostia-san Sebastián},
title = {Highlighting relevant concepts from Topic Signatures},
year = {}
}
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Abstract
This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applies a knowledge-based Word Sense Disambiguation algorithm to assign the most appro-priate WordNet sense to large sets of topically related words acquired from the web, named TSWEB. This Word Sense Disambiguation algorithm is the personalized PageRank algorithm implemented in UKB. This new method improves by automatic means the current content of WordNet by creating large volumes of new and accurate semantic relations between synsets. KnowNet was our first attempt towards the acquisition of large volumes of semantic relations. However, KnowNet had some limitations that have been overcomed with deepKnowNet. deepKnowNet disambiguates the first hundred words of all Topic Signatures from the web (TSWEB). In this case, the method highlights the most relevant word senses of each Topic Signature and filter out the ones that are not so related to the topic. In fact, the knowledge it contains outperforms any other resource when is empirically evaluated in a common framework based on a similarity task annotated with human judgements.
Keyphrases
topic signature relevant concept large volume first attempt relevant word sens accurate semantic relation human judgement automatic method current content large set semantic relation new method improves appro-priate wordnet sense accurate knowledge base semantic resource word sense disambiguation algorithm first hundred word knowledge-based word sense disambiguation algorithm personalized pagerank algorithm automatic mean similarity task common framework
