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

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 728–736, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Combining Lexical Semantic Resources with Question & Answer Archives for Translation-Based Answer Finding Delphine Bernhard and Iryna Gurevych Ubiquitous Knowledge Processing (UKP) Lab Computer Science Department Technische Universit¨at Darmstadt, Hochschulstraße 10 D-64289 Darmstadt, Germany http://www.ukp.tu-darmstadt.de/ Abstract Monolingual translation probabilities have recently been introduced in retrieval mod- els to solve the lexical gap problem. They can be obtained by training statisti- cal translation models on parallel mono- lingual 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 re- sources. We compare monolingual trans- lation 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 probabil- ities obtained (i) are comparable to tradi- tional semantic relatedness measures and (ii) significantly improve the results over the query likelihood and the vector-space model for answer finding. 1 Introduction The lexical gap (or lexical chasm) often observed between queries and documents or questions and answers is a pervasive problem both in Informa- tion Retrieval (IR) and Question Answering (QA). This problem arises from alternative ways of con- veying the same information, due to synonymy or paraphrasing, and is especially severe for re- trieval over shorter documents, such as sentence retrieval or question retrieval in Question & An- swer archives. Several solutions to this problem have been proposed including query expansion (Riezler et al., 2007; Fang, 2008), query refor- mulation or paraphrasing (Hermjakob et al., 2002; Tomuro, 2003; Zukerman and Raskutti, 2002) and semantic information retrieval (M¨uller et al., 2007). Berger and Lafferty (1999) have formulated a further solution to the lexical gap problem con- sisting in integrating monolingual statistical trans- lation models in the retrieval process. Monolin- gual translation models encode statistical word as- sociations which are trained on parallel monolin- gual corpora. The major drawback of this ap- proach lies in the limited availability of truly par- allel monolingual corpora. In practice, training data for translation-based retrieval often consist in question-answer pairs, usually extracted from the evaluation corpus itself (Riezler et al., 2007; Xue et al., 2008; Lee et al., 2008). While collection- specific translation models effectively encode sta- tistical word associations for the target document collection, it also introduces a bias in the evalua- tion and makes it difficult to assess the quality of the translation model per se, independently from a specific task and document collection. In this paper, we propose new kinds of datasets for training domain-independent mono- lingual translation models. We use the defini- tions and glosses provided for the same term by different lexical semantic resources to auto- matically train the translation models. This ap- proach has been very recently made possible by …

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