A Microfeature-based Scheme for Modelling Semantics

Abstract

One fundamental problem of natural language processing is word sense disambiguation. Solving this problem involves the integration of multiple knowledge sources: syntactic, semantic, and pragmatic. Recerit work has shown how this problem can be modelled as a constraint satisfaction process between competing syntactic and semantic structures. We have defined and implemented a "locally-distributed" microfeature based model called MIBS, that uses a distributed short-term memory (STM) composed of microfeatures to represent the underlying sentence semantics. This work represents an improvement over previous work, as it provides a natural language understanding system a means to dynamically determine the current context and adjust its relationship with the sentences that follow. Here, the meaning of a word is represented not as a symbol in some semantic net, but as a collection of smaller features. The values of the microfeatures in STM vary dynamically as the sentence is processed, reflecting the system's "settling" in on the sentence's meaning. In addition they represent an automatic context mechanism that helps the system to disambiguate the sentences that follow.