Deep learning for entity analysis : a thesis submitted in partial fulfilment for the degree of Doctor of Philosophy in Computer Science at the School of Natural and Computational Sciences, Massey University, Albany, New Zealand

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dc.confidentialEmbargo : Noen_US
dc.contributor.advisorWang, Ruili
dc.contributor.authorHou, Feng
dc.date.accessioned2021-03-01T20:22:56Z
dc.date.accessioned2021-06-28T20:43:43Z
dc.date.available2021-03-01T20:22:56Z
dc.date.available2021-06-28T20:43:43Z
dc.date.issued2021
dc.description.abstractOur research focuses on three sub-tasks of entity analysis: fine-grained entity typing (FGET), entity linking and entity coreference resolution. We aim at improving FGET and entity linking by exploiting the document-level type constraints and improving entity linking and coreference resolution by embedding fine-grained entity type information. To extract more efficient feature representations and offset label noises in the datasets for FGET, we propose three transfer learning schemes: (i) transferring sub-word embeddings to generate more efficient out-of-vocabulary (OOV) embeddings for mentions; (ii) using a pre-trained language model to generate more efficient context features; (iii) using a pre-trained topic model to transfer the topic-type relatedness through topic anchors and select confusing fine-grained types at inference time. The pre-trained topic model can offset the label noises without retreating to coarse-grained types. To reduce the distinctiveness of existing entity embeddings and facilitate the learning of contextual commonality for entity linking, we propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings. FGS2EE first uses the embeddings of semantic type words to generate semantic entity embeddings, and then combines them with existing entity embeddings through linear aggregation. Based on our entity embeddings, we have achieved new state-of-the-art performance on two of the five out-domain test sets for entity linking. Further, we propose a method, DOC-AET, to exploit DOCument-level coherence of named entity mentions and anonymous entity type (AET) words/mentions. We learn embeddings of AET words from the AET words’ inter-paragraph co-occurrence matrix. Then, we build AET entity embeddings and document AET context embeddings using the AET word embeddings. The AET coherence are computed using the AET entity embeddings and document context embeddings. By incorporating such coherence scores, DOC-AET has achieved new state-of-the-art results on three of the five out-domain test sets for entity linking. We also propose LASE (Less Anisotropic Span Embeddings) schemes for coreference resolution. We investigate the effectiveness of these schemes with extensive experiments. Our ablation studies also provide valuable insights about the contextualized representations. In summary, this thesis proposes four deep learning approaches for entity analysis. Extensive experiments show that we have achieved state-of-the-art performance on the three sub-tasks of entity analysis.en_US
dc.identifier.urihttp://hdl.handle.net/10179/16462
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectNatural language processing (Computer science)en
dc.subjectEntity-relationship modelingen
dc.subjectMachine learningen
dc.subject.anzsrc460208 Natural language processingen
dc.subject.anzsrc461103 Deep learningen
dc.titleDeep learning for entity analysis : a thesis submitted in partial fulfilment for the degree of Doctor of Philosophy in Computer Science at the School of Natural and Computational Sciences, Massey University, Albany, New Zealanden_US
dc.typeThesisen_US
massey.contributor.authorHou, Fengen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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