Corpus-based statistical sense resolution

Abstract

The three corpus-based statistical sense resolution methods studied here attempt to infer the correct sense of a polysemous word by using knowledge about patterns of word cooccurrences. The techniques were based on Bayesian decision theory, neural networks, and content vectors as used in information retrieval. To understand these methods better, we posed s very specific problem: given a set of contexts, each containing the noun line in a known sense, construct a classifier that selects the correct sense of line for new contexts. To see how the degree of polysemy affects performance, results from three-and slx-sense tasks are compared.