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
EstadoPublicada - 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

Serie de la publicación

NombreProceedings of the National Conference on Artificial Intelligence
Volumen2

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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