Instance-Based Question Answering
Abstract
During recent years, question answering (QA) has grown from simple passage retrieval and information extraction to very complex approaches that incorporate deep question and document analysis, reasoning, planning, and sophisticated uses of knowledge resources. Most existing QA systems combine rule-based, knowledge-based and statistical components, and are highly optimized for a particular style of questions in a given language. Typical question answering approaches depend on specific ontologies, resources, processing tools, document sources, and very often rely on expert knowledge and rule-based components. Furthermore, such systems are very difficult to re-train and optimize for different domains and languages, requiring considerable time and human effort. We present a fully statistical, data-driven, instance-based approach to question answering (IBQA) that learns how to answer new questions from similar training questions and their known correct answers. We represent training questions as points in a multi-dimensional space and cluster them according to different granularity, scatter, and similarity metrics. From each individual cluster we automatically learn an answering strategy for finding answers to questions. When answering a new question that is covered by several clusters, multiple answering strategies are simultaneously employed. The resulting answer confidence combines elements such as each strategy's estimated probability of success, cluster similarity to the new question, cluster size, and cluster granularity. The IBQA approach obtains good performance on factoid and definitional questions, comparable to the performance of top systems participating in official question answering evaluations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2006
- Accession Number
- ADA462538
Entities
People
- Lucian V. Lita
Organizations
- Carnegie Mellon University