Syntax, Preference and Right Attachment Yorick Wilks, Xiuming Huang & Dan Fass Computing Research Laboratory New Mexico State University Las Cruces, NM, USA 88003 ABSTRACT The paper claims that the right attachment rules for phrases originally suggested by Frazier and Fodor are wrong, and that none of the subsequent patchings of the rules by syntactic methods have improved the situation. For each rule there are perfectly straightforward and indefinitely large classes of simple counterexamples. We then examine suggestions by Ford et a!., Schubert and Hirst which are quasi-semantic in nature and which we consider ingenious but unsatisfactory. We offer a straightforward solution within the framework of preference semantics, and argue that the principal issue is not the type and nature of information required to get appropriate phrase attachments, but the issue of where to store the information and with what processes to apply it. We present a prolog implementation of a best first algorithm covering the data and contrast it with closely related ones, all of which are based on the preferences of nouns and prepositions, as well as verbs.

The paper discusses the origins and structure of Preference Semantics, a procedural and computational system for extracting the meaning structure of natural language texts, based on notions of “maximal semantic density” and coherence. The basic representational structures and procedures of Preference Semantics are described, as well as the forms these notions have taken in the work of others.

This dissertation describes a computational system for the analysis of English prose under the Preference Semantics theory of language understanding. The two main areas of investigation are these: (1) the design and implementation of a series of programs for extracting semantic information from a machine-readable dictionary, with this semantic information in a form suitable for use by a subsequent semantic analysis program, and (2) the design and implementation of a semantic analysis program that accepts short English texts and creates a corresponding representation from them. The resulting representation is in a suitable form, such that other Artificial Intelligence programs can use it as a knowledge source.