Word Sense Embedded in Geometric Spaces - From Induction to Applications using Machine Learning

Abstract: Words are not detached individuals but part of a beautiful interconnected web of related concepts, and to capture the full complexity of this web they need to be represented in a way that encapsulates all the semantic and syntactic facets of the language. Further, to enable computational processing they need to be expressed in a consistent manner so that similar properties are encoded in a similar way. In this thesis dense real valued vector representations, i.e. word embeddings, are extended and studied for their applicability to natural language processing (NLP). Word embeddings of two distinct flavors are presented as part of this thesis, sense aware word representations where different word senses are represented as distinct objects, and grounded word representations that are learned using multi-agent deep reinforcement learning to explicitly express properties of the physical world while the agents learn to play Guess Who?. The empirical usefulness of word embeddings are evaluated by employing them in a series of NLP related applications, i.e. word sense induction, word sense disambiguation, and automatic document summarisation. The results show great potential for word embeddings by outperforming previous state-of-the-art methods in two out of three applications, and achieving a statistically equivalent result in the third application but using a much simpler model than previous work.