QUALIFIER: Question Answering by Lexical Fabric and External Resources

QUALIFIER: Question Answering by Lexical Fabric and External Resources Hui Yang Department of Computer Science National University of Singapore 3 Science Drive 2, Singapore 117543 yangh@comp.nus.edu.sg Tat-Seng Chua Department of Computer Science National University of Singapore 3 Science Drive 2, Singapore 117543 chuats@comp.nus.edu.sg Abstract One of the major challenges in TREC- style question-answering (QA) is to over- come the mismatch in the lexical repre- sentations in the query space and document space. This is particularly se- vere in QA as exact answers, rather than documents, are required in response to questions. Most current approaches over- come the mismatch problem by employ- ing either data redundancy strategy through the use of Web or linguistic re- sources. This paper investigates the inte- gration of lexical relations and Web knowledge to tackle this problem. The re- sults obtained on TREC11 QA corpus in- dicate that our approach is both feasible and effective. 1 Introduction Open domain Question Answering (QA) is an information retrieval paradigm that is attracting increasing attention from the information re- trieval (IR), information extraction (IE), and natural language processing (NLP) communities (AAAI Spring Symposium Series 2002, ACL- EACL 2002). A QA system retrieves concise answers to open-domain natural language ques- tions, where a large text collection (termed the QA corpus) is used as the source for these an- swers. Contrary to traditional IR tasks, it is not acceptable for a QA system to retrieve a full document, or a paragraph, in response to a ques- tion. Contrary to traditional IE tasks, no pre- specified domain restrictions are placed on the questions, which may be of any type and in any topic. Modern QA systems must therefore com- bine the strengths of traditional IR and NLP/IE to provide an apposite way to answering questions. The QA task in the TREC conference series (Voorhees 2002) has motivated much of the re- cent works focusing on fact-based, short-answer questions. Examples of such questions include: "Who is Tom Cruise married to?" or "How many chromosomes does a human zygote have?". For the most recent TREC-11 conference, the task consists of 500 questions posed over a QA corpus containing more than one million newspaper arti- cles. Instead of previous years' 50-byte or 250- byte text fragments, exact answers are expected from the QA corpus with supports of documen- tary evidences. One of the major challenges in TREC-style QA is to overcome the mismatch in the lexical representations between the query space and document space. This mismatch, also known as the QA gap, is caused by the differences in the set of terms used in the question formulation and answer strings in the corpus. Given a source, such as the QA corpus, that contains only a rela- tively small number of answers to a query, we are faced with the difficulty to map the questions to answers by way of uncovering the complex lexi- cal, syntactic, or semantic relationships between the question and the answer strings. Recent redundancy-based approaches (Brill et al 2002, Clarke et al …

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