Representations of Idioms for Natural Language Processing: Idiom type and token identification, Language Modelling and Neural Machine Translation

An idiom is a multiword expression (MWE) whose meaning is noncompositional, i.e., the meaning of the expression is different from the meaning of its individual components. Idioms are complex constructions of language used creatively across almost all text genres. Idioms pose problems to natural language processing (NLP) systems due to their non-compositional nature, and the correct processing of idioms can improve a wide range of NLP systems. Current approaches to idiom processing vary in terms of the amount of discourse history required to extract the features necessary to build representations for the expressions. These features are, in general, statistics extracted from the text and often fail to capture all the nuances involved in idiom usage. We argue in this thesis that a more flexible representations must be used to process idioms in a range of idiom related tasks. We demonstrate that high-dimensional representations allow idiom classifiers to better model the interactions between global and local features and thereby improve the performance of these systems with regard to processing idioms. In support of this thesis we demonstrate that distributed representa-