Word Sense Disambiguation Features for Taxonomy Extraction



Título del documento: Word Sense Disambiguation Features for Taxonomy Extraction
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000560359
ISSN: 1405-5546
Autores: 1
Instituciones: 1National Research University Higher School of Economics, Moscow. Rusia
Año:
Periodo: Jul-Sep
Volumen: 22
Número: 3
Paginación: 871-880
País: México
Idioma: Inglés
Tipo de documento: Artículo
Resumen en inglés Many NLP tasks, such as fact extraction, coreference resolution etc, rely on existing lexical taxonomies or ontologies. One of the possible approaches to create a lexical taxonomy is to extract taxonomic relations from a monolingual dictionary or encyclopedia: a semi-formalized resource designed to contain many relations of this kind. Word-sense disambiguation (WSD) is a mandatory tool for such approaches. The quality of the extracted taxonomy greatly depends on WSD results. Most WSD approaches can be posed as machine learning tasks. For this sake feature representation ranges from collocation vectors as in Lesk algorithm or neural network features in Word2Vec to highly specialized word sense representation models such as AdaGram. In this work we apply several WSD algorithms to dictionary definitions. Our main focus is the influence of different approaches to extract WSD features from dictionary definitions on WSD accuracy.
Disciplinas: Ciencias de la computación
Palabras clave: Inteligencia artificial,
Contexto,
Taxonomía,
Palabras,
Desambiguación,
Extracción,
Semántica,
Procesamiento de lenguaje natural
Keyword: Context,
Taxonomy,
Words,
Artificial intelligence,
Disambiguation,
Extraction,
Semantics,
Natural language processing
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