ClaC: Semantic Relatedness of Words and Phrases
This addresses the need for accurate semantic relatedness metrics in natural language processing, though it appears incremental as it combines existing models.
The paper tackled the problem of measuring phrasal semantic relatedness for NLP applications by presenting three approaches, including a hybrid method that achieved an F-measure of 77.4% on semantic similarity evaluation.
The measurement of phrasal semantic relatedness is an important metric for many natural language processing applications. In this paper, we present three approaches for measuring phrasal semantics, one based on a semantic network model, another on a distributional similarity model, and a hybrid between the two. Our hybrid approach achieved an F-measure of 77.4% on the task of evaluating the semantic similarity of words and compositional phrases.