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New Directions in Question Answering

Volume

Papers from the 2003 AAAI Spring Symposium

Contents

A dream since the first investigations into artificial intelligence has been to converse with a machine in natural language, in part to get answers to questions. Question answering (QA) promises an important new way of information access for all, a natural step beyond the keyword query and document retrieval of today’s web search. Several significant question answering activities currently underway include the ARDA AQUAINT Program, a TREC QA track, the ARDA NRRC (nrrc.mitre.org) summer 2002 workshops on temporal and multiple perspective question answering, and an LREC 2002 workshop to develop a question answering roadmap. In spite of many activities, the potential richness of question answering has still only been partially investigated and includes challenges such as: the heterogeneity of questions and sources (e.g., multilingual, multimedia); a broad range of (query and document) processing possibilities and answer retrieval mechanisms; methods for answer extraction, integration, and presentation generation; and new application areas (e.g., question answering from manuals and for help desks). Accordingly, this symposium focuses on new directions in the burgeoning area of question answering. Question answering is challenging, in part, because it lies at the intersection of several scientific fields including natural language processing (understanding and generating natural language text), information retrieval (query formulation, document analysis, relevancy feedback), and human computer interaction (interface design, user modeling). Figure 1 illustrates the relation of the three areas and their intersection in systems that support question answering. Several additional scientific disciplines may support question answering are not shown, such as knowledge representation and reasoning for question and answer analysis, or recommender technology to find preferred answers, or multimodal information processing to help extract answers from audio or video sources, or information visualization for results display.