Use of Metadata for Question Answering and Novelty Tasks

CL Research’s question-answering system for TREC 2003 was modified away from reliance on database technology to the core underlying technology of using massive XML-tagging for processing both questions and documents. This core technology was then extended to participate in the novelty task. This technology provides many opportuinities for experimenting with various approaches to question answering and novelty determination. For the QA track, we submitted one run and our overall main task score was 0.075, with scores of 0.070 for factoid questions, 0.000 for list questions, and 0.160 for definition questions. For the passage task, we submitted two runs, our better score was 0.119 for the factoid questions. These scores were all considerably below the medians for these tasks. We have implemented further routines since our official submission, improving our scores to 0.18 and 0.23 for the exact answer and passages tasks, respectively. For the Novelty track, we submitted four runs for task 1, one run for task 2, five runs for task 3, and one run for task 4; our submissions for tasks 2 and 4 were identical. For task 1, our best run received an F-score of 0.483 for relevant sentences and 0.410 for new sentences. For task 2, our F-score was 0.788 for new sentences. For task 3, our best F-score was 0.558 for relevant sentences and 0.419 for new sentences. For task 4, our F-score was 0.655 for new sentences. On average, our Fscores were somewhat above the medians on all tasks. We describe our system and examine our results from the perspective of exploiting the metadata in the XML tags.