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Feng Hou

Doctor of Philosophy, (Computer Science)
Study Completed: 2021
College of Sciences


Thesis Title
Deep Learning for Entity Analysis

Natural (human) language texts have many mentions of entities which can be a person, location, or organization. Entity analysis identifies and analyses different aspects of entity mentions for understanding natural language. Mr Hou developed novel deep learning methods to improve the computer programs for three sub-tasks of entity analysis: classifying entity mentions into fine-grained types, linking entity mentions to concrete entities in a knowledge base, and clustering co-referent entity mentions. For fine-grained classification, three transfer learning schemes were developed to learn more efficient representations and offset label noises in the datasets. For entity linking, typed entity representations were proposed to improve the learning of contextual commonality, and anonymous entity mentions were exploited to capture the document-level coherence of entities. For clustering co-referent entity mentions, more diversified mention representations were generated to distinguish related but distinct entities.

Professor Ruili Wang
Professor Yi Zhou