Mostra el registre d'ítem simple
Highlighting relevant concepts from Topic Signatures
dc.contributor.author | Cuadros Oller, Montserrat |
dc.contributor.author | Padró, Lluís |
dc.contributor.author | Rigau Claramunt, German |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2012-06-08T11:19:12Z |
dc.date.available | 2012-06-08T11:19:12Z |
dc.date.created | 2012 |
dc.date.issued | 2012 |
dc.identifier.citation | Cuadros, M.; Padró, L.; Rigau, G. Highlighting relevant concepts from Topic Signatures. A: International Conference on Language Resources and Evaluation. "LREC2012". Istanbul: 2012. |
dc.identifier.uri | http://hdl.handle.net/2117/15988 |
dc.description.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 |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Llenguatges de programació |
dc.subject.lcsh | Computational linguistics |
dc.subject.lcsh | Semantics --Data processing |
dc.title | Highlighting relevant concepts from Topic Signatures |
dc.type | Conference report |
dc.subject.lemac | Semàntica computacional |
dc.contributor.group | Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural |
dc.rights.access | Open Access |
local.identifier.drac | 10507590 |
dc.description.version | Postprint (published version) |
local.citation.author | Cuadros, M.; Padró, L.; Rigau, G. |
local.citation.contributor | International Conference on Language Resources and Evaluation |
local.citation.pubplace | Istanbul |
local.citation.publicationName | LREC2012 |