Finding the topical anchors of a context using lexical cooccurrence data

Abstract

Lexical cooccurrence in textual data is not uniformly random. The statistics inferred from the term-cooccurrence data enable us to model dependencies between terms as graphs, somewhat resembling the way semantic memory is organised in human beings. In this paper we look at cooccurrence patterns to identify topical anchors of a given context. Topical anchors are those terms whose semantics represent the topic of the whole context. This work is based on computing a stationary distribution in the cooccurrence graph. Topical anchors were computed on a set of 100 contexts and were also evaluated by 86 volunteers and the results show that the algorithm correctly identifies the topical anchors around 62% of the time. Copyright 2009 ACM.