Highlighting relevant concepts from Topic Signatures
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/15988
Tipus de documentText en actes de congrés
Data publicació2012
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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 appropriate 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
CitacióCuadros, M.; Padró, L.; Rigau, G. Highlighting relevant concepts from Topic Signatures. A: International Conference on Language Resources and Evaluation. "LREC2012". Istanbul: 2012.
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highlighting.pdf | 688,4Kb | Visualitza/Obre |