MURAX: A Robust Linguistic Approach For Question Answering Using An On-line Encyclopedia

Abstract

Robust linguistic methods are applied to the task of answering closed-class questions using a corpus of natural language. The methods are illustrated in a broad domain: answering general-knowledge questions using an on-line encyclopedia. A closed-class question is a question stated in natural language, which assumes some definite answer typified by a noun phrase rather than a procedural answer. The methods hypothesize noun phrases that are likely to be the answer, and present the user with relevant text in which they are marked, focussing the user's attention appropriately. Furthermore, the sentences of matching text that are shown to the user are selected to confirm phrase relations implied by the question, rather than being selected solely on the basis of word frequency. The corpus is accessed via an information retrieval (IR) system that supports boolean search with proximity constraints. Queries are automatically constructed from the phrasal content of the question, and passed to the IR system to find relevant text. Then the relevant text is itself analyzed; noun phrase hypotheses are extracted and new queries are independently made to confirm phrase relations for the various hypotheses. The methods are currently being implemented in a system called MURAX and although this process is not complete, it is sufficiently advanced for an interim evaluation to be presented.