Current Institution: Carnegie Mellon University

Abstract:
Learning Semantic Frames for Natural Language Understanding

Automatic extraction of meaning representations from natural language text is crucial for applications such as question answering, knowledge base construction, and intelligent dialog agents. Semantic frames – structured representations of prototypical scenarios people talk about (e.g., Who does What to Whom, When, Where, and How) — have been widely used in Natural Language Processing (NLP) systems to represent content conveyed in text. Automatic extraction of semantic frames from text is challenging, as it requires reasoning about various kinds of semantic elements, e.g., entities, attributes, events, and relations, and the correct interpretation of their meanings often requires background knowledge and relevant context. My research addresses these two challenges by developing statistical models that can jointly reason about different types of semantic elements, while taking into account their semantic dependencies based on context and background knowledge. In contrast to existing approaches, our joint model predicts semantic frames in a unified framework instead of a pipeline of independent classifiers. This leads to state-of-the-art performance on various natural language understanding tasks, including event extraction (i.e., extracting what happened, who was involved, when, and where), event coreference resolution (i.e., predicting which event descriptions refer to the same event), and fine-grained opinion extraction (i.e., predicting the polarity of an opinion, its holder, and its target).

Bio:
Bishan Yang is a Post-doctoral Fellow at Carnegie Mellon University. Her research develops machine learning techniques for natural language understanding. She is currently working with Prof. Tom Mitchell on developing a machine reading system that automatically reads documents and makes predictions based on the meanings conveyed in text as well as background knowledge. She received her PhD from Cornell University in 2016. Her PhD thesis is on automatic extraction of opinions and events expressed in text. Prior to that, she received her BS and MS in Computer Science from Peking University, China. She is a recipient of the Olin Fellowship from Cornell University.