Combining Lexical Semantic Resources with Question & Answer Archives for Translation-Based Answer Finding

Abstract

Monolingual translation probabilities have recently been introduced in retrieval models to solve the lexical gap problem. They can be obtained by training statistical translation models on parallel monolingual corpora, such as question-answer pairs, where answers act as the “source” language and questions as the “target” language. In this paper, we propose to use as a parallel training dataset the definitions and glosses provided for the same term by different lexical semantic resources. We compare monolingual translation models built from lexical semantic resources with two other kinds of datasets: manually-tagged question reformulations and question-answer pairs. We also show that the monolingual translation probabilities obtained (i) are comparable to traditional semantic relatedness measures and (ii) significantly improve the results over the query likelihood and the vector-space model for answer finding.