Using a semantic concordance for sense identification

This paper proposes benchmarks for systems of automatic sense identification using a textual corpus in which open-class words had been tagged both syntactically and semantically to explore three statistical strategies for sense identification: a guessing heuristic, a most-frequent heuristic; and a co-occurrence heuristic.

Abstract

This paper proposes benchmarks for systems of automatic sense identification. A textual corpus in which open-class words had been tagged both syntactically and semantically was used to explore three statistical strategies for sense identification: a guessing heuristic, a most-frequent heuristic, and a co-occurrence heuristic. When no information about sense-frequencies was available, the guessing heuristic using the numbers of alternative senses in WordNet was correct 45% of the time. When statistics for sense-frequencies were derived from a semantic concordance, the assumption that each word is used in its most frequently occurring sense was correct 69% of the time; when that figure was calculated for polysemous words alone, it dropped to 58%. And when a co-occurrence heuristic took advantage of prior occurrences of words together in the same sentences, little improvement was observed. The semantic concordance is still too small to estimate the potential limits of a co-occurrence heuristic.