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McKelvey School of Engineering

Brown School

Label-Efficient Textual Knowledge Extraction and Utilization

Wednesday, March 1, 2023 | 11:30 AM

Jiaxin Huang

PhD Candidate

Department of Computer Science

University of Illinois, Urbana-Champaign

With tremendous amounts of texts across the Internet nowadays, various Natural Language Processing (NLP) systems are built to help people seek for valuable knowledge from massive corpora, by performing knowledge-intensive tasks like text retrieval, concept organization, commonsense reasoning, and question answering. Despite the remarkable success, most existing NLP systems still rely on large amounts of task-specific training data, which are costly to obtain.

My research designs principled approaches for label-efficient, knowledge-based NLP applications which rely on minimal human supervision. In this talk, I will introduce a general framework for textual knowledge extraction and utilization: (1) concept ontology construction by transforming generic linguistic knowledge encoded in pre-trained language models into hierarchical structures connecting entities; (2) entity extraction by replacing manual prompt template designs with automatic soft verbalizer learning; (3) commonsense reasoning via entity knowledge prompting and iteratively optimizing reasoning paths generated by language models.

Event Type



McKelvey School of Engineering


Science & Technology

Computer Science & Engineering
Event Contact


Speaker Information

Jiaxin Huang is a final-year Ph.D. candidate in the Department of Computer Science at University of Illinois, Urbana-Champaign, fortunately advised by Prof. Jiawei Han. Jiaxin's research interests lie in text mining and natural language processing with minimal human supervision. Her recent research focuses on (1) using pre-trained language models to automatically extract domain-specific hierarchical concepts and entities for structured knowledge construction; (2) extracting human actionable knowledge such as commonsense reasoning by prompting and training language models via machine-generated explicit reasoning paths. She is a recipient of the Microsoft Research PhD Fellowship (2021-2023).

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