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One of the major challenges that NLP faces is metaphor detection, especiallyby automatic means, a task that becomes even more difficult for languageslacking in linguistic resources and tools. Our purpose is the automaticdifferentiation between literal and metaphorical meaning in authenticnon-annotated phrases from the Corpus of Greek Texts by means of computationalmethods of machine learning. For this purpose the theoretical background ofdistributional semantics is discussed and employed. Distributional SemanticsTheory develops concepts and methods for the quantification and classificationof semantic similarities displayed by linguistic elements in large amounts oflinguistic data according to their distributional properties. In accordancewith this model, the approach followed in the thesis takes into account thelinguistic context for the computation of the distributional representation ofphrases in geometrical space, as well as for their comparison with thedistributional representations of other phrases, whose function in speech isalready "known" with the objective to reach conclusions about their literal ormetaphorical function in the specific linguistic context. This procedure aimsat dealing with the lack of linguistic resources for the Greek language, as thealmost impossible up to now semantic comparison between "phrases", takes theform of an arithmetical comparison of their distributional representations ingeometrical space.

Neural embeddings for metaphor detection in a corpus of Greek texts