Date of Award
Dissertation (Access Restricted)
Doctor of Philosophy
Max M Louwerse
Thematic role labeling has been an area of interest in several domains of natural language processing such as language generation and information retrieval. Extracting thematic roles efficiently and with minimal supervision affects all the above areas. Most of the existing computational methods for thematic role extraction are highly dependent on human annotated corpora, and are driven by the rules generated by supervised processes. In this work I extracted thematic roles using a data-driven approach. I built an unsupervised language generation model inspired from the ADIOS model, to learn recurring substructures from language, and with minimal supervision learned the rules that are needed to identify thematic roles. I tested the consistency of the sub-structures encoding thematic role information over PropBank annotated sentences. Results indicated that sub-structures consistently hold semantic role information, and the method robustly showed that thematic role information could be extracted with minimal supervision.
Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Datla, Vivek Varma, "Date-Driven Approach For Thematic Role Extraction" (2014). Electronic Theses and Dissertations. 2231.