A Model of Syntactic Parsing Based on Domain-General Cognitive Mechanisms

A Model of Syntactic Parsing Based on Domain-General Cognitive Mechanisms Arthi Murugesan (muruga@rpi.edu) Nicholas Cassimatis (cassin@rpi.edu) Rensselaer Polytechnic Institute Department of Cognitive Science, 110 8 th Street Troy, NY 12180 USA Abstract The relationship between linguistic and nonlinguistic cogni- tion is an important area of study including the questions of language modularity and learnability. We believe that insights to this relationship can be obtained by implementing a precise computational model of sentence understanding within a gen- eral cognitive architecture. In this paper we have represented a wide-coverage grammar (Head-driven Phrase Structure Grammar) using non-linguistic representation. We have shown how grammar-specific representations like specifier, complement, modifier and gap map to domain-general event representations such as subgoals and temporal constraints. The paper thus demonstrates that domain-general cognitive mechanisms are sufficient and adequate for syntactic parsing. Keywords: Syntactic Parsing; domain-general mechanisms; HPSG; Polyscheme; modularity of language. Introduction The relationship between linguistic and nonlinguistic cogni- tion is an important question in cognitive science. One way of studying this topic is to ask if language is an independent module or integrated with the rest of cognition. Both ap- proaches have problems explaining the phenomenon. The modularity of language has been argued for by Fodor (1983) and Chomsky (1988). Both in computational linguistics and in cognitive modeling of sentence process- ing, modular approaches have trouble explaining the inter- action of people’s syntax-specific processing mechanisms and general inference mechanisms about the world. A number of sentences that we parse in daily life require vis- ual information, social awareness, higher reasoning and other complementary cognitive processes to obtain the sub- tle meanings, for example: seeing. Therefore, the mechanisms used to understand a seemingly simple sentence include language, vision, reason- ing and semantic knowledge. The modular theory doesn’t address the question raised by this phenomenon: if the mechanisms of language and other kinds of inferences are so different, how do they interact so tightly and so well? On the other hand, the non-modular approach claims that language is a part of and interacts freely with the greater cognitive system (Newell & Simon 1972; Marslen- Wilson and Tyler, 1989). The striking problem here, per- haps most visible in syntax, is that concepts involved in lan- guage (e.g., argument structure, head projection or agree- ment) and concepts involved in physical reasoning (e.g., gravity, time or place) seem to be very dissimilar and unre- lated. How could we use, for example, the same mechanism to solve an equation, plan a shortest route to a restaurant and determine the binding properties of a given pronoun? The lack of a detailed answer to these questions makes the modularity hypothesis more plausible. Cassimatis (2004) addressed this disparity by showing that aspects of language syntax such as categories, word order and constituency can be mapped onto domain- general cognitive structures. Although this work demon- strated that broad linguistic concepts have duals in domain- general cognition, it did not demonstrate that these high- level dualities were sufficient to map a sophisticated gram- matical theory onto domain-general cognitive structures. Addressing Modularity with Cognitive Models of Sentence Understanding I saw a star with a telescope. I saw an astronomer with a telescope. We believe that designing a sentence parsing system within a cognitive architecture will aid us to get a better insight of language integration with cognition. Our approach is driven by the hypotheses described in this section. Language processing uses the same mechanisms as the other forms of cognition. We accept the evidence that language is highly integrated with the rest of cognition. In addition to the commonalities between syntactic and physical structure outlined in Cassimatis (2004), there are several reasons for this hypothesis in other parts of lan- guage. For example, Bloom (2000) and Tomasello, Kruger, and Ratner (1993) provide experimental results that show the general theory of mind learning also aids in learning words. Jackendoff (1990) demonstrates how concepts from physical reasoning can be used to formalize semantics of verbs in many fields. Clark (1996) analyzes many aspects of language use as a species of social interaction. All this In these sentences, the telescope could be the instrument used to view the object (star or astronomer) or the telescope could be seen near (with) the object. However, typically people infer the star to be viewed through the telescope and the astronomer to be near the telescope. Disambiguation can be attributed to both the visual perception of the telescope and the reasoning that an astronomer uses the instrument frequently while watching an astronomer through the tele- scope is improbable. This reasoning also requires knowl- edge about astronomers, their instruments and the action of