No abstract

We explore a hybrid approach for Chinese definitional question answering by combining deep linguistic analysis with surface 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 annotation 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 definitional 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 answering 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 biographical questions; 4) the most useful linguistic features are copulas and appositives; relations also play an important role; only some propositions convey vital facts.

We evaluated large-vocabulary continuous-speech recognizer performance as a function of recognizer tuning parameters for 4 recognition task domains (location, date, time, yes/no) and two different applications (e.g. over-the-telephone reservations) that had some task domains in common. After defining a cost function that included false reject, false accept, and misrecognition errors, we determined optimum parameter values for each domain. The optimum parameter settings differed significantly across domains and even across applications for the same domain. Using a single set of parameter values for all of the tasks in an application can lead to substantial cost penalties for some individual tasks. These results suggest that there can be substantial benefit in using task-specific tuned recognition parameters. We describe a methodology and set of supporting tools for efficiently performing taskspecific tuning.