A semantic-context ranking approach for community-oriented english lexical simplification

Abstract

Lexical simplification under a given vocabulary scope for specified communities would potentially benefit many applications such as second language learning and cognitive disabilities education. This paper proposes a new concise ranking strategy for incorporating semantic and context for lexical simplification to a restricted scope. Our approach utilizes WordNet-based similarity calculation for semantic expansion and ranking. It then uses Part-of-Speech tagging and Google 1T 5-gram corpus for context-based ranking. Our experiments are based on a publicly available data sets. Through the comparison with baseline methods including Google Word2vec and four-step method, our approach achieves best F1 measure as 0.311 and Oot F1 measure as 0.522, respectively, demonstrating its effectiveness in combining semantic and context for English lexical simplification.