Basic Concepts of Lexical Resource Semantics

Semanticists use a range of highly expressive logical langu ges to characterize the meaning of natural language expressions. The logical languages are usually taken from an i nventory of standard mathematical systems, with which generative linguists are familiar. They are, thus, easily a ccessible beyond the borders of a given framework such as Categorial Grammar, Lexical Functional Grammar, or Govern me t and Binding Theory. Linguists working in the HPSG framework, on the other hand, often use rather idiosync ratic and specialized semantic representations. Their choice is sometimes motivated by computational applicatio ns n parsing, generation, or machine translation. Natural ly, the intended areas of application influence the design of sem antic representations. A typical property of semantic representations in HPSG that is concerned with computational a pplications is underspecification, and other properties come from the particular unification or constraint solving a l orithms that are used for processing grammars. While the resulting semantic representations have properties th at are motivated by, and are adequate for, certain practical applications, their relationship to standard languages is sometimes left on an intuitive level. In addition, the theor etical and ontological status of the semantic representations is o ften neglected. This vagueness tends to be unsatisfying to many semanticists, and the idiosyncratic shape of the seman tic representations confines their usage to HPSG. Since their entire architecture is highly dependent on HPSG, hard ly anyone working outside of that framework is interested in studying them. With our work on Lexical Resource Semantics (LRS), we want to contribute to the investigation of a number of important theoretical issues surrounding semantic repres ntations and possible ways of underspecification. While LR S is formulated in a constraint-based grammar environment an d t kes advantage of the tight connection between syntax proper and logical representations that can easily be achie ved in HPSG, the architecture of LRS remains independent from that framework, and combines attractive properties of various semantic systems. We will explore the types of semantic frameworks which can be specified in Relational Spe ciate Re-entrant Language (RSRL), the formalism that we choose to express our grammar principles, and we evaluate the semantic frameworks with respect to their potential for providing empirically satisfactory analyses of typica l problems in the semantics of natural languages. In LRS, we want to synthesize a flexible meta-theory that can be applied to different interesting semantic representation languag es and make computing with them feasible. We will start our investigation with a standard semantic rep resentation language from natural language semantics, Ty2 (Gallin, 1975). We are well aware of the debate about the a ppropriateness of Montagovian-style intensionality for the analysis of natural language semantics, but we belie ve that it is best to start with a semantic representation tha t most generative linguists are familiar with. As will become cl ar in the course of our discussion, the LRS framework is a meta-theory of semantic representations, and we believ e that it is suitable for various representation languages, This paper can be regarded as a snapshot of our work on LRS. It w as written as material for the authors’ course Constraint-based Combinatorial Semanticsat the 15th European Summer School in Logic, Language and Inf ormation in Vienna in August 2003. It is meant as background r eading and as a basis of discussion for our class. Its air of a work in progressis deliberate. As we see continued development in LRS, its ap plication to a wider range of languages and empirical phenomena, and espec ially the implementation of an LRS module as a component of th e TRALE grammar development environment; we expect further modifications a d refinements to the theory. The implementation of LRS is rea liz d in collaboration with Gerald Penn of the University of Toronto. We would like t o thank Carmella Payne for proofreading various versions of this paper.