Title: Annotating, Learning, and Representing Shallow Semantic Structure
Advisor: Luke Zettlemoyer
Supervisory Committee: Luke Zettlemoyer (Chair), Gina-Anne Levow (GSR, Linguistics), Yejin Choi, and Noah Smith
Abstract: One key challenge to understanding human language is to find out the word to word semantic relations, such as “who does what to whom”, “when”, and “where”. Semantic role labeling (SRL) is the widely studied challenge of recovering such predicate-argument structure, typically for verbs. SRL is designed to be consistent across syntactic annotation and to some extent, language independent, which can potentially benefit downstream applications such as natural language inference, machine translation and summarization. However, the performance of SRL system is limited by the amount of training data, further impeding its usage in downstream applications. My generals work is three folds: 1) collecting more SRL data using natural language driven QA annotation, 2) using end-to-end neural models to accurately predict SRL, and 3) proposing a unified framework for predicting and representing SRL structure to improve performance on downstream applications.