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 representing 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. M apping 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 deep analysis to the fore, we have chosen to work on a task we call “knowledge tracking” that cannot be accomplished without interpretation of the source text. The goal of knowledge tracking is to identify the relationship of the content of a new document to the content of previously collected documents. Knowledge tracking can thus be viewed as a particular kind of question answering for a set of implicit questions. It provides useful functionality even when applied to a medium-size collection and can therefore serve as a laboratory where deep processing is feasible. Results on this task can help to extend the capabilities of many question-answering systems.