Mohammed Alliheedi, PhD candidate
David R. Cheriton School of Computer Science
The central focus of this thesis is rhetorical moves in biochemistry articles. Kanoksilapatham has provided a descriptive theory of rhetorical moves that extends Swales’ CARS model to the complete biochemistry article. The thesis begins the construction of a computational model of this descriptive theory. Attention is placed on the Methods section of the articles.
We hypothesize that because authors’ argumentation closely follows their experimental procedure, procedural verbs may be the guide to understanding the rhetorical moves. Our work proposes an extension to the normal (i.e., VerbNet) semantic roles especially tuned to this domain. A major contribution is a corpus of Method sections that have been marked up for rhetorical moves and semantic roles. The writing style of this genre tends to occasionally omit semantic roles, so another important contribution is a prototype ontology that provides experimental procedure knowledge for the biochemistry domain.
Our computational model employs machine learning to build its models for the semantic roles and rhetorical moves, validated against a gold standard reflecting the annotation of these texts by human experts. We provide significant insights into how to derive these annotations, and as such have contributions as well to the general challenge of producing markups in the domain of biomedical science documents, where specialized knowledge is required.
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