Abstract Question answering is an important task of natural language processing. Unification-based grammars have emerged as formalisms for reasoning about natural text. Head-driven Phrase Structure Grammar is a unification-based grammar that is capable of representing syntax and semantics in a uniform fashion allowing one to reason about syntax and semantics simultaneously. Classical unification, on which the unification- based grammars rely, is strict in the sense that it requires a perfect agreement between the terms being unified. In practise, data is seldom error-free and can contain inconsistent information. The theory of relaxed unification is a new theory that relaxes the constraints of the classical unification. Relaxed unification tolerates errors and inconsistencies in the data and facilitates reasoning under uncertainty. We use the relaxed unification formalism in the head-driven phrase structure grammar to build a question-answering advanced prototype that addresses some of the pitfalls of the current question- answering systems. Faculty of Computer Science Tony Abou-Assaleh, Nick Cercone, and Vlado Kešelj INTRODUCTION Motivation –Head-driven Phrase Structure Grammar (HPSG) can represent both syntax and semantics –Question Answering (QA) plays an important role in Natural Language Processing (NLP) –Relaxed Unification enables HPSG to process data that is inconsistent, incomplete, and uncertain. Objectives –Identify limitations of current QA systems –Develop a framework to overcome these limitations –Build a QA advanced prototype –Objectively evaluate the prototype against other approaches Classical Unification –Two terms, t and u, are unifiable if and only if there exists a substitution  such that t  = u . Relaxed Unification –Relaxed Term consists of variables and sets of functions, where sets can be empty. Function arguments are relaxed terms. E.g. { f ( { a }, { h ( { b } ) } ) }. –two terms, t and u, are always unifiable with a unifying substitution  such that t  = u . –Application-specific correctness measure evaluates the result. RELAXED UNIFICATION HPSG AND QUESTION ANSWERING Query –“How much could you rent a Volkswagen bug for in 1966?” Relevant Text –“… you could rent a Volkswagen bug for $1 a day.” Answer –“$1 a day.” EVALUATION Environment –Text REtrieval Conference (TREC) Question Answering Track. –Open-domain, closed-class questions. –Shallow semantic analysis is some times required. –Restricted QA: only factoid and definition questions. Approach –Use an Information Retrieval (IR) search engine to locate relevant passages. –Use HPSG to parse the question and generate a query. –Use HPSG to parse the passage. –Use relaxed unification to locate a query match in the passage. –Compute the correctness of the result using a metric to rank the answers. References Abou-Assaleh, Tony and Cercone, Nick and Keselj, Vlado. Towards the Theory of Relaxed Unification. In Proceedings of the 14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003, volume LNAI 2871 of Lecture Notes in Computer Science, Springer, Maebashi City, Japan, October , Kešelj, Vlado. Question Answering using Unification-based Grammar. In Advances in Artificial Intelligence, AI 2001, volume LNAI 2056 of Lecture Notes in Computer Science, Springer, Ottawa, Canada, June OutputInput Documents Collection Relevant Passages Question IR Search Engine HPSG Parser Query HPSG Parser Parsed Passages Relaxed Unification Matches Evaluation Ranked Matches Extraction Ranked Answers