Prior methodologies for understanding spatial language have treated literal expressions such as “Mary pushed the car over the edge” differently from metaphorical extensions such as “Mary’s job pushed her over the edge”. We demonstrate a methodology for standardizing literal and metaphorical meanings, by building on work in Lexical Conceptual Structure (LCS), a general-purpose representational component used in machine translation. We argue that spatial predicates naturally extend into other fields (e.g., circumstantial or temporal), and that LCS provides both a framework for distinguishing spatial from non-spatial, and a system for finding metaphorical meaning extensions. We start with MetaNet (MN), a large repository of conceptual metaphors, condensing 197 spatial entries into sixteen top-level categories of motion frames. Using naturally occurring instances of English push , and expansions of MN frames, we demonstrate that literal and metaphorical extensions exhibit patterns predicted and represented by the LCS model.

This paper describes a refinement to our procedure for porting lexical conceptual structure (LCS) into new languages. Specifically we describe a two-step process for creating candidate thematic grids for Mandarin Chinese verbs, using the English verb heading the VP in the subde_nitions to separate senses, and roughly parsing the verb complement structure to match thematic structure templates. We accomplished a substantial reduction in manual effort, without substantive loss. The procedure is part of a larger process of creating a usable lexicon for interlingual machine translation from a large on-line resource with both too much and too little information.

We describe a theoretical investigation into the semantic space described by our interlingua (IL), which currently has 191 main verb classes divided into 434 subclasses, represented by 237 distinct Lexical Conceptual Structures (LCSs). Using the model of aspect in Olsen (1994; 1997)—monotonic aspectual composition—we have identified 71 aspectually basic subclasses that are associated with one or more of 68 aspectually non-basic classes via some lexical (“type-shifting”) rule (Bresnan, 1982; Pinker, 1984; Levin and Rappaport Hovav, 1995). This allows us to refine the IL and address certain computational and theoretical issues at the same time. (1) From a linguistic viewpoint, the expected benefits include a refinement of the aspectual model in (Olsen, 1994; Olsen, 1997) (which provides necessary but not sufficient conditions for aspectual com- position), and a refinement of the verb classifications in (Levin, 1993); we also expect our approach to eventually produce a systematic definition (in terms of LCSs and compositional operations) of the precise meaning components responsible for Levin's classification. (2) Computationally, the lexicon is made more compact.

In this paper we report on experiments using WordNet synset tags to evaluate the semantic properties of the verb classes cataloged by Levin (1993). This paper represents ongoing research begun at the University of Pennsylvania (Rosenzweig and Dang, 1997; Palmer, Rosenzweig, and Dang, 1997) and the University of Maryland (Dorr and Jones, 1996b; Dorr and Jones, 1996a; Dorr and Jones, 1996c). Using WordNet sense tags to constrain the intersection of Levin classes, we avoid spurious class intersections introduced by homonymy and polysemy (run a bath, run a mile). By adding class intersections based on a single shared sense-tagged word, we minimize the impact of the non-exhaustiveness of Levin’s database (Dorr and Olsen, 1996; Dorr, To appear). By examining the syntactic properties of the intersective classes, we provide a clearer picture of the relationship between WordNet/EuroWordNet and the LCS interlingua for machine translation and other NLP applications.