Learning to rank effective paraphrases from query logs for community question answering

Alejandro Figueroa, Günter Neumann

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

18 Citas (Scopus)

Resumen

We present a novel method for ranking query paraphrases for effective search in community question answering (cQA). The method uses query logs from Yahoo! Search and Yahoo! Answers for automatically extracting a corpus of paraphrases of queries and questions using the query-question click history. Elements of this corpus are automatically ranked according to recall and mean reciprocal rank, and then used for learning two independent learning to rank models (SVMRank), whereby a set of new query paraphrases can be scored according to recall and MRR. We perform several automatic evaluation procedures using cross-validation for analyzing the behavior of various aspects of our learned ranking functions, which show that our method is useful and effective for search in cQA.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Páginas1099-1105
Número de páginas7
EstadoPublicada - 2013
Evento27th AAAI Conference on Artificial Intelligence, AAAI 2013 - Bellevue, WA, Estados Unidos
Duración: 14 jul 201318 jul 2013

Otros

Otros27th AAAI Conference on Artificial Intelligence, AAAI 2013
PaísEstados Unidos
CiudadBellevue, WA
Período14/07/1318/07/13

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial

Citar esto

Figueroa, A., & Neumann, G. (2013). Learning to rank effective paraphrases from query logs for community question answering. En Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013 (pp. 1099-1105)