Combining Deep Linguistics Analysis and Surface Pattern Learning: A Hybrid Approach to Chinese Definitional Question Answering

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 307–314, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Combining Deep Linguistics Analysis and Surface Pattern Learning: A Hybrid Approach to Chinese Definitional Question Answering Fuchun Peng, Ralph Weischedel, Ana Licuanan, Jinxi Xu BBN Technologies 50 Moulton Street, Cambridge, MA, 02138 � fpeng, rweisched, alicuan, jxu � @bbn.com Abstract We explore a hybrid approach for Chinese definitional question answering by com- bining deep linguistic analysis with sur- face pattern learning. We answer four questions in this study: 1) How helpful are linguistic analysis and pattern learning? 2) What kind of questions can be answered by pattern matching? 3) How much an- notation is required for a pattern-based system to achieve good performance? 4) What linguistic features are most useful? Extensive experiments are conducted on biographical questions and other defini- tional questions. Major findings include: 1) linguistic analysis and pattern learning are complementary; both are required to make a good definitional QA system; 2) pattern matching is very effective in an- swering biographical questions while less effective for other definitional questions; 3) only a small amount of annotation is required for a pattern learning system to achieve good performance on biographi- cal questions; 4) the most useful linguistic features are copulas and appositives; re- lations also play an important role; only some propositions convey vital facts. 1 Introduction Due to the ever increasing large amounts of online textual data, learning from textual data is becom- ing more and more important. Traditional document retrieval systems return a set of relevant documents and leave the users to locate the specific information they are interested in. Question answering, which combines traditional document retrieval and infor- mation extraction, solves this problem directly by returning users the specific answers. Research in textual question answering has made substantial ad- vances in the past few years (Voorhees, 2004). Most question answering research has been focus- ing on factoid questions where the goal is to return a list of facts about a concept. Definitional ques- tions, however, remain largely unexplored. Defini- tional questions differ from factoid questions in that the goal is to return the relevant “answer nuggets” of information about a query. Identifying such an- swer nuggets requires more advanced language pro- cessing techniques. Definitional QA systems are not only interesting as a research challenge. They also have the potential to be a valuable comple- ment to static knowledge sources like encyclopedias. This is because they create definitions dynamically, and thus answer definitional questions about terms which are new or emerging (Blair-Goldensoha et al., 2004). One success in factoid question answering is pattern based systems, either manually con- structed (Soubbotin and Soubbotin, 2002) or ma- chine learned (Cui et al., 2004). However, it is unknown whether such pure pattern based systems work well on definitional questions where answers are more diverse. Deep linguistic analysis has been found useful in factoid question answering (Moldovan et al., 2002) and has been used for …

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