A Logic for Semantic Interpretation

A Logic for Semantic Interpretation I Eugene Charniak and Robert Goldman Department of Computer Science Brown University, Box 1910 Providence RI 02912 Abstract We propose that logic (enhanced to encode probability information) is a good way of characterizing semantic in- terpretation. In support of this we give a fragment of an axiomatization for word-sense disambiguation, noun- phrase (and verb) reference, and case disambiguation. We describe an inference engine (Frail3) which actually takes this axiomatization and uses it to drive the semantic interpretation process. We claim three benefits from this scheme. First, the interface between semantic interpreta- tion and pragmatics has always been problematic, since all of the above tasks in general require pragmatic infer- ence. Now the interface is trivial, since both semantic interpretation and pragmatics use the same vocabulary and inference engine. The second benefit, related to the first, is that semantic guidance of syntax is a side effect of the interpretation. The third benefit is the elegance of the semantic interpretation theory. A few simple rules capture a remarkable diversity of semantic phenomena. I. Introduction The use of logic to codify natural language syntax is well known, and many current systems can parse directly off their axiomatizations (e.g.,)[l]. Many of these systems simultaneously construct an intermediate "logical form" using the same machinery. At the other end of language processing, logic is a well-known tool for expressing the pragmatic information needed for plan recognition and speech act recognition [2-4]. In between these extremes logic appears much less. There has been some movement in the direction of placing semantic interpretation on a more logical footing [5,6], but it is nothing like what has happened at the extremes of the ~anguage understanding process. To some degree this is understandable. These "mid- dle" parts, such as word-sense disambiguation, noun phrase reference, case disambiguation, etc. are notori- ously difficult, and poorly understood, at least compared to things like syntax, and the construction of interme- diate logical form. Much of the reason these areas are l This work has been supported in part by the National Science Foundation under grants IST 8416034 and IST 8515005 and Office ~)f Nav~l Research under grant N00014-79-C-0529. so dark is that they are intimately bound up with prag- matic reasoning. The correct sense of a word depends on context, as does pronoun resolution, etc. Here we rectify this situation by presenting an ax- iomatization of fragment of semantic interpretation, no- tably including many aspects previously excluded: word- sense disambiguation, noun-phrase reference determina- tion, case determination, and syntactic disambiguation. Furthermore we describe an inference engine, Frail3, which can use the logical formulation to carry out seman- tic interpretation. The description of Frail3 is brief, since the present paper is primarily concerned with semantic interpretation. For a more detailed description, see [7]. The work closest to what we present is that by Hobbs [5]; however, he handles only noun-phrase reference from the above list, and he does not consider intersentential influences at all. Our system, Wimp2 (which uses Frail3), is …

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