Versatile neural approaches to more accurate and robust topic segmentation
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Xing, Linzi
issued: 2024-02-23T09:10:13Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Topic segmentation, as a fundamental NLP task, has been proposed and systematically studied since the 1980s and received increased attention in recent years due to the surge in big data. It aims to unveil the coarse-grained semantic structure of long unstructured documents by automatically dividing them into shorter, topically coherent segments.The coarse-grained structure provided by topic segmentation has been proven to not only enhance human reading efficiency but also play a vital role in other natural language understanding tasks, such as text summarization, question answering, and dialogue modeling. Before the neural era, early computational models for topic segmentation typically adhered to unsupervised paradigms with lexical cohesion directly derived from the input, yet their performance was notably limited. With the evolution of deep learning and enhanced computational capabilities, neural models have delivered significant progress in performance. Nevertheless, inadequate coherence modeling, in terms of both explicitness and reliability in these neural approaches, prevents them from emerging as more accurate and robust solutions for topic segmentation. Additionally, the growing prevalence of multi-modal data content across social media platforms has heightened the need for topic segmentation to traverse beyond mere text, extending into videos. Motivated by the challenges and needs mentioned above, in this thesis, we direct our efforts towards enhancing neural topic segmentation for two types of documents: text and video. To overcome the inadequate coherence modeling (explicitness and reliability) in neural topic segmenters for text, we propose a series of methods that either more explicitly model coherence patterns or leverage coherence signals encoded in related auxiliary tasks, notably discourse parsing and language modeling. For video content, we explore to extend neural topic segmenters, originally designed for text, into a multi-modal setting which is …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87475
note: Versatile Neural Approaches to More Accurate and RobustTopic SegmentationbyLinzi XingB. Mgt, Soochow University, 2016M. Sc, University of Colorado Boulder, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Computer Science)The University of British Columbia(Vancouver)February 2024© Linzi Xing, 2024The following individuals certify that they have read, and recommend to the Facultyof Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Versatile Neural Approaches to More Accurate and Robust Topic Seg-mentationsubmitted by Linzi Xing in partial fulfillment of the requirements for the degree ofDoctor of Philosophy in Computer Science.Examining Committee:Giuseppe Carenini, Professor, Computer Science, UBCSupervisorRaymond Ng, Professor, Computer Science, UBCSupervisory Committee MemberDavid Poole, Professor, Computer Science, UBCUniversity ExaminerMarcin Morzycki, Associate Professor, Department of Linguistics, UBCUniversity ExaminerSujian Li, Associate Professor, Computer Science, UBCExternal ExaminerAdditional Supervisory Committee Members:Laks V.S. Lakshmanan, Professor, Computer Science, UBCSupervisory Committee MemberiiAbstractTopic segmentation, as a fundamental NLP task, has been proposed and systemati-cally studied since the 1980s and received increased attention in recent years due tothe surge in big data. It aims to unveil the coarse-grained semantic structure of longunstructured documents by automatically dividing them into shorter, topically co-herent segments. The coarse-grained structure provided by topic segmentation hasbeen proven to not only enhance human reading efficiency but also play a vital rolein other natural language understanding tasks, such as text summarization, questionanswering, and dialogue modeling.Before the neural era, early computational models for topic segmentation typi-cally adhered to unsupervised paradigms with le…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0440128
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Carenini, Giuseppe
type: Text
identifier URI: http://hdl.handle.net/2429/87475