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Title: Investigations into semantic role labeling of propbank and nombank
Keywords: Natural Language Processing Machine Learning
Issue Date: 1-Aug-2006
Citation: JIANG ZHENG PING (2006-08-01). Investigations into semantic role labeling of propbank and nombank. ScholarBank@NUS Repository.
Abstract: The task of Semantic Role Labeling (SRL) concerns the determination of the generic semantic roles of constituents in asentence. This thesis focuses on SRL based on the PropBank and NomBank corpora. Specifically, it addresses the following two questions:1. How do we exploit the interdependence of semantic arguments in a predicate-argument structure to improve an SRL system?2. How do we make use of the newly available NomBank corpus to build an SRL system that produces predicate-argument structures for nouns?To address the first question, this thesis conducted experiments to explore various ways of exploiting the interdependence of semantic arguments to effectively improve the SRL accuracy on PropBank.For the second question, this thesis adapted a PropBank-based SRL system to the SRL task of NomBank. Structures unique to NomBank's annotation are captured as additional features in a maximum entropy classification model to improve the adapted system.
Appears in Collections:Master's Theses (Open)

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