# Neural embeddings for metaphor detection in a corpus of Greek texts

## TL;DR

This paper presents a machine learning approach using neural embeddings and distributional semantics to automatically detect metaphors in Greek texts, addressing resource scarcity in Greek NLP.

## Contribution

It introduces a novel method combining neural embeddings and distributional semantics for metaphor detection in Greek, a low-resource language.

## Key findings

- Effective differentiation between literal and metaphorical phrases
- Utilizes geometric space representations for semantic comparison
- Addresses resource limitations in Greek NLP

## Abstract

One of the major challenges that NLP faces is metaphor detection, especially by automatic means, a task that becomes even more difficult for languages lacking in linguistic resources and tools. Our purpose is the automatic differentiation between literal and metaphorical meaning in authentic non-annotated phrases from the Corpus of Greek Texts by means of computational methods of machine learning. For this purpose the theoretical background of distributional semantics is discussed and employed. Distributional Semantics Theory develops concepts and methods for the quantification and classification of semantic similarities displayed by linguistic elements in large amounts of linguistic data according to their distributional properties. In accordance with this model, the approach followed in the thesis takes into account the linguistic context for the computation of the distributional representation of phrases in geometrical space, as well as for their comparison with the distributional representations of other phrases, whose function in speech is already "known" with the objective to reach conclusions about their literal or metaphorical function in the specific linguistic context. This procedure aims at dealing with the lack of linguistic resources for the Greek language, as the almost impossible up to now semantic comparison between "phrases", takes the form of an arithmetical comparison of their distributional representations in geometrical space.