Structured Knowledge Discovery from Massive Text Corpus

by   Chenwei Zhang, et al.

Nowadays, with the booming development of the Internet, people benefit from its convenience due to its open and sharing nature. A large volume of natural language texts is being generated by users in various forms, such as search queries, documents, and social media posts. As the unstructured text corpus is usually noisy and messy, it becomes imperative to correctly identify and accurately annotate structured information in order to obtain meaningful insights or better understand unstructured texts. On the other hand, the existing structured information, which embodies our knowledge such as entity or concept relations, often suffers from incompleteness or quality-related issues. Given a gigantic collection of texts which offers rich semantic information, it is also important to harness the massiveness of the unannotated text corpus to expand and refine existing structured knowledge with fewer annotation efforts. In this dissertation, I will introduce principles, models, and algorithms for effective structured knowledge discovery from the massive text corpus. We are generally interested in obtaining insights and better understanding unstructured texts with the help of structured annotations or by structure-aware modeling. Also, given the existing structured knowledge, we are interested in expanding its scale and improving its quality harnessing the massiveness of the text corpus. In particular, four problems are studied in this dissertation: Structured Intent Detection for Natural Language Understanding, Structure-aware Natural Language Modeling, Generative Structured Knowledge Expansion, and Synonym Refinement on Structured Knowledge.


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