Zoom: https://illinois.zoom.us/j/7030162755?pwd=QkY2OHI2K1ZFdjY3S3FwcU5FT05tUT09
Abstract:
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.
Bio:
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).
https://wiki.illinois.edu/wiki/display/grad/PILOT+SEMINAR
