A Contextual Semantic-Based Approach for Domain-Centric Lexicon Expansion

Abstract

This paper presents a contextual semantic-based approach for expansion of an initial lexicon containing domain-centric seed words. Starting with a small lexicon containing some domain-centric seed words, the proposed approach models text corpus as a weighted word-graph, where the initial weight of a node (word) represents the contextual semantic-based association between the node and the target domain, and the weight of an edge represents the co-occurrence frequency of the respective nodes. The semantic-based association between a node and the target domain is calculated as a function of three contextual semantic-based association metrics. Thereafter, a random walk-based modified PageRank algorithm is applied on the weighted graph to rank and select the most relevant terms for domain-centric lexicon expansion. The proposed approach is evaluated over five datasets, and found to perform significantly better than three baselines and three state-of-the-art approaches.