PhD Defence • Computational Linguistics | Artificial Intelligence • A Knowledge Representation for, and an Application to Requirements Elicitation of, Rhetorical Figures of Perfect Lexical Repetition

Friday, November 7, 2025 2:00 pm - 5:00 pm EST (GMT -05:00)

Please note: This PhD defence will take place in DC 2310 and online.

Yetian Wang, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Daniel Berry, Grant Weddell

Rhetorical figures, such as rhyme and metaphor, affect human discourse by providing essential semantic and pragmatic information that generate a set of attentional effects such as salience, aesthetic pleasure, and memorability, that enhance the receiver’s attention. Ploke is one kind of rhetorical figure, that of perfect lexical repetition, which is a word or phrase that repeats with the same form and meaning in a passage. Rhetorical figures, including plokes, are largely ignored in natural language processing (NLP) and artificial intelligence (AI).

This thesis aims to take two steps towards AIs’ being able to handle plokes as they occur in natural language. It first develops a knowledge representation model of the general concept of Ploke in the form of an ontology that represents the classification of Ploke, the forms of plokes, and the neurocognitive affinities that affect attention. This ontology will help AIs to understand and generate plokes. The ontology proposed in this thesis is able to represent the related knowledge of ploke and its subtypes. The ontology is able also to represent the neurocognitive affinities of ploke and its subtypes by representing their relations to various types of perfect lexical repetition characterized by the positions in which the repetitions occur. After observing that rhetorical figures are used to enhance persuasive discourse, the thesis hypothesizes that a requirements elicitation interview that uses plokes is more effective than one that does not. It then describes a test of this hypothesis in which the interviews were conducted by a simulated AI elicitation bot, which used some plokes in half of its interviews and avoided plokes entirely in the other half of its interviews. The experiment showed that the interview questions and statements conveyed by the simulated AI elicitation bot in its ploke-using requirements elicitation interviews were easier to recognize by the interviewees and were more memorable to them than those in its ploke-avoiding requirements elicitation interviews.


To attend this PhD defence in person, please go to DC 2310. You can also attend virtually on Zoom.