Cracking the Figurative Code: A Survey of Metaphor Detection Techniques (PREPRINT)

Abstract

Metaphor Detection is a crucial area of study in computational linguistics and natural language processing, as it enables the understanding and communication of abstract ideas through the use of concrete imagery. This survey paper aims to provide an overview of the current state-of-the-art approaches that tackle this issue, and analyze trends in the domain across years. The survey recapitulates the existing methodologies for metaphor detection, highlighting their key contributions and limitations. The methods are assigned three broad categories, namely feature-engineering based, traditional deep learning-based, and transformer-based approaches. An analysis of strengths and weaknesses of each category is showcased. Furthermore, the paper explores the annotated corpora that have been developed to facilitate the development and evaluation of metaphor detection models. By providing a comprehensive overview of the work already done and the research gaps present in pre-existing literature, this survey paper aims to help future research endeavors, and thus contribute to the advancement of metaphor detection methodologies.

Keywords

Metaphor Detection, Natural Language Processing, Linguistic Analysis, Computational Linguistics, Lexical Semantics

Presented at: 2nd International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2023)