Question Answering Passage Retrieval and Re-ranking Using N-grams and SVM



Título del documento: Question Answering Passage Retrieval and Re-ranking Using N-grams and SVM
Revista: Computación y sistemas
Base de datos: PERIÓDICA
Número de sistema: 000411075
ISSN: 1405-5546
Autors: 1
2
Institucions: 1Universite de Tunis, Institut Superieur de Gestion de Tunis, El Manar, Tunis. Túnez
2Universite de Carthage, Institut des Hautes Etudes Commerciales de Carthage, Carthage. Túnez
Any:
Període: Jul-Sep
Volum: 20
Número: 3
Paginació: 483-494
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés Over the last few decades, with the meteoric rise of Information Technology, Question Answering (QA) has attracted more attention and has been extremely explored. Indeed, several QA systems are based on a passage retrieval engine which aims to deliver a set of passages that are most likely to contain a relevant response to a question stated in natural language. In an attempt to enhance the performance of existing QASs by increasing the number of generated correct answers and ensure their relevance, we propose a novel approach for retrieving and re-ranking passages based on n-grams and SVM models. The core principle is to first rely on the dependency degree of n-gram words of the query in the passage to retrieve correct passages. Then, an SVM based model is used to improve passage ranking incorporating various lexical, syntactic and semantic similarity measures. Emperical evaluation performed with the CLEF dataset demonstrates the merits of our approach: the results obtained by our implemented system transcend that of other previously proposed ones
Disciplines Ciencias de la computación,
Literatura y lingüística
Paraules clau: Procesamiento de datos,
Lingüística aplicada,
Lingüística computacional,
Recuperación de información,
Búsqueda de respuestas,
Recuperación de pasajes,
Semántica
Keyword: Computer science,
Literature and linguistics,
Data processing,
Applied linguistics,
Computing linguistics,
Information retrieval,
Question answering,
Passage retrieval,
Semantics
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