Knowledge Tracking: Answering Implicit Questions

Research on Question Answering has produced an arsenal of useful techniques for detecting answers that are explicitly present in the text of a collection of documents. To move beyond current capabilities, effort must be directed toward analyzing the source documents and interpreting them by ting their content, abstracting away from the particular linguistic expressions used. The content representations enable reasoning based on what things mean rather than how they are phrased. Mapping accurately from natural language text to content representations requires deep linguistic analysis and proper treatment of ambiguity and contexts. Research in Question Answering has traditionally tried to circumvent these problems due to the lack of feasible solutions. We strongly believe that these problems can and must be tackled now: PARC’s deep NLP technology scales well, and our preliminary results with mapping to content representation are encouraging. In order to bring fundamental issues of dee...

• Type: Conference
• Year: 2003
• Where: NDQA
• Authors: Reinhard Stolle, Daniel G. Bobrow, Cleo Condoravdi, Richard S. Crouch, Valeria de Paiva
• Keywords: Content Representation, Education, Knowledge Tracking, NDQA 2003, Question Answering