  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.
