Towards a Self-Extending Parser

TOWARDS A SELF-EXTENDING PARSER Jaime G. Carbonell Department Of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 Abstract This paper discusses an approach to incremental learning in natural language processing. The technique of projecting and integrating semantic constraints to learn word definitions is analyzed as Implemented in the POLITICS system. Extensions and improvements of this technique are developed. The problem of generalizing existing word meanings and understanding metaphorical uses of words Is addressed In terms of semantic constraint Integration. 1. Introduction Natural language analysis, like most other subfields of Artificial Intelligence and Computational Linguistics, suffers from the fact that computer systems are unable to automatically better themselves. Automated learning ia considered a very difficult problem, especially when applied to natural language understanding. Consequently, little effort ha8 been focused on this problem. Some pioneering work in Artificial intelligence, such as AM [I] and Winston's learning system 1"2] strove to learn or discover concept descriptions in well-defined domains. Although their efforts produced interesting Ideas and techniques, these techniques do not fully extend to • domain as complex as natural language analysis. Rather than attempting the formidable task of creating a language learning system, I will discuss techniques for Incrementally Increasing the abilities of a flexible language analyzer. There are many tasks that can be considered "Incremental language learning". Initially the learning domain Is restricted to learning the meaning of new words and generalizing existing word definitions. There ere a number of A.I. techniques, and combinations of these techniques capable of exhibiting incremental learning behavior. I first discuss FOULUP and POLITICS, two programs that exhibit a limited capability for Incremental word learning. Secondly, the technique of semantic constraint projection end Integration, as Implemented in POLITICS, Is analyzed in some detail. Finally, I discuss the application of some general learning techniques to the problem of generalizing word definitions end understanding metaphors. 2. Learning From Script Expectations Learning word definitions In semantically-rich contexts Is perhaps one of the simpler tasks of incremental learning. Initially I confine my discussion to situations where the meaning of a word can be learned from the Immediately surrounding context. Later I relax this criterion to see how global context and multiple examples can help to learn the meaning of unknown words. The FOULUP program [3] learned the meaning of some unknown words in the context of applying s script to understand a story. Scripts [4, 5] are frame-like knowledge representations abstracting the important features and causal structure of mundane events. Scripts have general expectations of the actions and objects that will be encountered in processing a story. For Instance, the restaurant script expects to see menus, waitresses, and customers ordering and eating food (at different pre-specifled times In the story). FOULUP took advantage of these script expectations to conclude that Items referenced in the story, which were part of expected actions, were Indeed names of objects that the script expected to see. These expectations were used to form definitions of new words. For instance, FOULUP induced the meaning of "Rabbit" in, …

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