tailieunhanh - Báo cáo khoa học: "The Role of Lexico-Semantic Feedback in Open-Domain Textual Question-Answering"

This paper presents an open-domain textual Question-Answering system that uses several feedback loops to enhance its performance. These feedback loops combine in a new way statistical results with syntactic, semantic or pragmatic information derived from texts and lexical databases. The paper presents the contribution of each feedback loop to the overall performance of 76% human-assessed precise answers. | The Role of Lexico-Semantic Feedback in Open-Domain Textual Question-Answering Sanda Harabagiu Dan Moldovan Marius Pasca Rada Mihalcea Mihai Surdeanu Razvan Bunescu Roxana Girju Vasile Rus and Paul Morarescu Department of Computer Science and Engineering Southern Methodist University Dallas TX 75275-0122 sanda @ Abstract This paper presents an open-domain textual Question-Answering system that uses several feedback loops to enhance its performance. These feedback loops combine in a new way statistical results with syntactic semantic or pragmatic information derived from texts and lexical databases. The paper presents the contribution of each feedback loop to the overall performance of 76 human-assessed precise answers. 1 Introduction Open-domain textual Question-Answering Q A as defined by the TREC competitions1 is the task of identifying in large collections of documents a text snippet where the answer to a natural language question lies. The answer is constrained to be found either in a short 50 bytes or a long 250 bytes text span. Frequently keywords extracted from the natural language question are either within the text span or in its immediate vicinity forming a text paragraph. Since such paragraphs must be identified throughout voluminous collections automatic and autonomous Q A systems incorporate an index of the collection as well as a paragraph retrieval mechanism. Recent results from the TREC evaluations Kwok et al. 2000 Radev et al. 2000 Allen 1The Text REtrieval Conference TREC is a series of workshops organized by the National Institute of Standards and Technology NIST designed to advance the state-of-the-art in information retrieval IR et al. 2000 show that Information Retrieval IR techniques alone are not sufficient for finding answers with high precision. In fact more and more systems adopt architectures in which the semantics of the questions are captured prior to paragraph retrieval . Gaizauskas and Humphreys 2000 Harabagiu et al. .