Using Semantic Richness for Metaphor Detection using Deep Learning

Abstract

ABSTRACT The Natural Language Processing (NLP) encounters difficulties with metaphors, known for their creative and non-literal usage. Metaphors involve using words or phrases from one context in entirely different contexts, making the meaning less clear and requiring human interpretation for understanding. This dissertation places its focus on the semantic richness elements derived from the perceptual part of the semantic network. These elements serve as the main linguistic features integrated into vector representations. By extracting the semantic information encompassing concreteness, imageability, sensory experience, sentiment, and embodiment, this study seeks to explore the feasibility of detecting metaphors using deep learning models. The investigation is conducted using two experimental structures: sentence-level classification for the categorisation of entire sentences and word-level classification for individual words. These models are assessed across three metaphorical datasets: VUAMC, MOH-X, and TroFi. The main objective is to evaluate the impact of these semantic elements on the metaphor detection task, with the potential for enhancing model performance.