An Investigation Of Neural Embeddings For Coreference Resolution

Coreference Resolution is an important task in Natural Language Processing (NLP) and involves finding all the phrases in a document that refer to the same entity in the real world, with applications in question answering and document summarisation. Work from deep learning has led to the training of neural embeddings of words and sentences from unlabelled text. Word embeddings have been shown to capture syntactic and semantic properties of the words and have been used in POS tagging and NER tagging to achieve state of the art performance. Therefore, the key contribution of this paper is to investigate whether neural embeddings can be leveraged to overcome challenges associated with the scarcity of coreference resolution labelled datasets for benchmarking. We show, as a preliminary result, that neural embeddings improve the performance of a coreference resolver when compared to a baseline.