Category-specific models for ranking effective paraphrases in community Question Answering

Alejandro Figueroa, Günter Neumann

Resultado de la investigación: Contribución a una revistaArtículo

17 Citas (Scopus)

Resumen

Platforms for community-based Question Answering (cQA) are playing an increasing role in the synergy of information-seeking and social networks. Being able to categorize user questions is very important, since these categories are good predictors for the underlying question goal, viz. informational or subjective. Furthermore, an effective cQA platform should be capable of detecting similar past questions and relevant answers, because it is known that a high number of best answers are reusable. Therefore, question paraphrasing is not only a useful but also an essential ingredient for effective search in cQA. However, the generated paraphrases do not necessarily lead to the same answer set, and might differ in their expected quality of retrieval, for example, in their power of identifying and ranking best answers higher. We propose a novel category-specific learning to rank approach for effectively ranking paraphrases for cQA. We describe a number of different large-scale experiments using logs from Yahoo! Search and Yahoo! Answers, and demonstrate that the subjective and objective nature of cQA questions dramatically affect the recall and ranking of past answers, when fine-grained category information is put into its place. Then, category-specific models are able to adapt well to the different degree of objectivity and subjectivity of each category, and the more specific the models are, the better the results, especially when benefiting from effective semantic and syntactic features.

Idioma originalInglés
Páginas (desde-hasta)4730-4742
Número de páginas13
PublicaciónExpert Systems with Applications
Volumen41
N.º10
DOI
EstadoPublicada - 1 ago 2014

Áreas temáticas de ASJC Scopus

  • Ingeniería (todo)
  • Informática aplicada
  • Inteligencia artificial

Huella Profundice en los temas de investigación de 'Category-specific models for ranking effective paraphrases in community Question Answering'. En conjunto forman una huella única.

  • Citar esto