We present an approach to query expansion in answer retrieval that uses Statistical Machine Translation (SMT) techniques to bridge the lexical gap between questions and answers. SMT-based query expansion is done by i) using a full-sentence paraphraser to introduce synonyms in context of the entire query, and ii) by translating query terms into answer terms using a full-sentence SMT model trained on question-answer pairs. We evaluate these global, context-aware query expansion techniques on tfidf retrieval from 10 million question-answer pairs extracted from FAQ pages. Experimental results show that SMTbased expansion improves retrieval performance over local expansion and over retrieval without expansion. 1

The vast amounts of information readily available on the World Wide Web can be effectively used for question answering in two fundamentally different ways. In the federated approach, techniques for handling semistructured data are applied to access Web sources as if they were databases, allowing large classes of common questions to be answered uniformly. In the distributed approach, largescale text-processing techniques are used to extract answers directly from unstructured Web documents. Because the Web is orders of magnitude larger than any human-collected corpus, question answering systems can capitalize on its unparalleled-levels of data redundancy. Analysis of real-world user questions reveals that the federated and distributed approaches complement each other nicely, suggesting a hybrid approach in future question answering systems.

Answer Extraction (AE) systems retrieve phrases in textual documents that directly answer natural language questions. AE over technical m...

The most recent TREC workshop contained a track for evaluating domain-independent question answering (QA) systems. In addition to fostering research on the QA task, the track was used to investigate whether the evaluation methodology used for document retrieval is appropriate for a different natural language processing task. This paper describes the implementation of the track, focusing on how the test questions were selected and the responses judged. By using multiple assessors to independently judge the responses, we verified that assessors do have legitimate differences of opinion as to the correctness of a response, even for the fact-based, short-answer questions used in the track. The Text REtrieval Conference (TREC) is a workshop series organized by the U.S. National Institute of Standards and Technology (NIST) designed to advance the state-ofthe -art in text retrieval (Voorhees, 2000). The most recent TREC, held in November 1999, included a "track" on question answering where t...