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Natural Language Processing and Text Mining with Graph-Structured Representations

  • Author / Creator
    Liu, Bang
  • Natural Language Processing (NLP) and understanding aims to read from unformatted text to accomplish different tasks. As a first step, it is necessary to represent text as a simplified model. Traditionally, Vector Space Model (VSM) is most commonly used, in which text is represented as a bag of words. Recent years, word vectors learned by deep neural networks are also widely used. However, the underlying linguistic and semantic structures of text pieces cannot be expressed and exploited in these representations. Graph is a natural way to capture the connections between different text pieces, such as entities, sentences, and documents. To overcome the limits in vector space models, we combine deep learning models with graph-structured representations for various tasks in NLP and text mining. Such combinations help to make full use of both the structural information in text and the representation learning ability of deep neural networks. Specifically, we make contributions to the following NLP tasks: First, we introduce tree-based/graph-based sentence/document decomposition techniques to align sentence/document pairs, and combine them with Siamese neural network and graph convolutional networks (GCN) to perform fine-grained semantic relevance estimation. Based on them, we propose Story Forest system to automatically cluster streaming documents into fine-grained events, while connecting related events in growing trees to tell evolving stories. Story Forest has been deployed into Tencent QQ Browser for hot event discovery. Second, we propose ConcepT and GIANT systems to construct a user-centered, web-scale ontology, containing a large number of heterogeneous phrases conforming to user attentions at various granularities, mined from the vast volume of web documents and search click logs. We introduce novel graphical representation and combine it with Relational-GCN to perform heterogeneous phrase mining and relation identification. GIANT system has been deployed into Tencent QQ Browser for news feeds recommendation and searching, serving more than 110 million daily active users. It also offers document tagging service to WeChat. Third, we propose Answer-Clue-Style-aware Question Generation to automatically generate diverse and high-quality question-answer pairs from unlabeled text corpus at scale by mimicking the way a human asks questions. Our algorithms combine sentence structure parsing with GCN and Seq2Seq-based generative model to make the "one-to-many" question generation close to "one-to-one" mapping problem. A major part of our work has been deployed into real world applications in Tencent and serves billions of users.

  • Subjects / Keywords
  • Graduation date
    Spring 2020
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/r3-n2ph-v492
  • License
    Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.