ClaC: Semantic Relatedness of Words and Phrases
Computation and Language
2017-08-22 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1708.05801,
title = {ClaC: Semantic Relatedness of Words and Phrases},
author = {Reda Siblini and Leila Kosseim},
journal= {arXiv preprint arXiv:1708.05801},
year = {2017}
}
Comments
In Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013),June, Atlanta, Georgia, USA, pp. 108-113