Maximum entropy context models for ranking biographical answers to open-domain definition questions

Alejandro Figueroa, John Atkinson

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

In the context of question-answering systems, there are several strategies for scoring candidate answers to definition queries including centroid vectors, bi-term and context language models. These techniques use only positive examples (i.e., descriptions) when building their models. In this work, a maximum entropy based extension is proposed for context language models so as to account for regularities across non-descriptions mined from web-snippets. Experiments show that this extension outperforms other strategies increasing the precision of the top five ranked answers by more than 5%. Results suggest that web-snippets are a cost-efficient source of non-descriptions, and that some relationships extracted from dependency trees are effective to mine for candidate answer sentences.

Idioma originalInglés
Título de la publicación alojadaAAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference
Páginas1173-1179
Número de páginas7
Volumen2
EstadoPublicada - 2 nov 2011
Evento25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA, Estados Unidos
Duración: 7 ago 201111 ago 2011

Otros

Otros25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
País/TerritorioEstados Unidos
CiudadSan Francisco, CA
Período7/08/1111/08/11

Áreas temáticas de ASJC Scopus

  • Software
  • Inteligencia artificial

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