An Approach to Web-Scale Named-Entity Disambiguation

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Luís Sarmento (Author) Other; Alexander Kehlenbeck (Author) Other; Eugénio Oliveira (Author) FEUP; Lyle Ungar (Author) Other

Conference proceedings International

Title: Machine Learning and Data Mining in Pattern Recognition

Pages: 689-703

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

Leipzig, Germany, 23-25 July, 2009

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 0 Citations

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Informatics

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15