An Approach to Web-Scale Named-Entity Disambiguation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Luís Sarmento (Autor) Outra; Alexander Kehlenbeck (Autor) Outra; Eugénio Oliveira (Autor) FEUP; Lyle Ungar (Autor) Outra

Ata de Conferência Internacional

Título: Machine Learning and Data Mining in Pattern Recognition

Páginas: 689-703

6th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Leipzig, Germany, 23-25 July, 2009

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 0 Citações

INSPEC

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Físicas > Ciência de computadores > Informática

Outras Informações

ID Authenticus: P-003-R7R

DOI: 10.1007/978-3-642-03070-3_52

Abstract (EN): We present a multi-pass clustering approach to large scale. wide-scope named-entity disambiguation (NED) oil collections of web pages. Our approach Uses name co-occurrence information to cluster and hence disambiguate entities. and is designed to handle NED on the entire web. We show that on web collections, NED becomes increasing), difficult as the corpus size increases, not only because of the challenge of scaling the NED algorithm, but also because new and surprising facets of entities become visible in the data. This effect limits the potential benefits for data-driven approaches of processing larger data-sets, and suggests that efficient clustering-based disambiguation methods for the web will require extracting more specialized information front documents.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15