Word Sense Disambiguation using a Bidirectional LSTM

In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations

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

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

Extractive Summarization using Continuous Vector Space Models

Automatic summarization can help users extract the most important pieces of information from the vast amount of text digitized into electronic form everyday. Central to automatic summarization is the notion of similarity between sentences in text. In this paper we propose the use of continuous vector representations for semantically aware representations of sentences as a basis for measuring similarity. We evaluate different compositions for sentence representation on a standard dataset using the ROUGE evaluation measures. Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization