Electronic Theses and Dissertations

Identifier

1294

Borhan Samei

Date

2014

Document Type

Thesis

Degree Name

Master of Science

Major

Computer Science

Committee Chair

Vasile Rus

Committee Member

Arthur C. Graesser

Committee Member

Scott D. Fleming

Abstract

Speech act classification is the task of detecting speakers' intentions in discourse. Speech acts are based on the language as action theory according to which when we say something we do something. Speech act classification has various application in natural language processing and dialogue-based intelligent systems. In this thesis, we propose machine learning models for speech act classification that account for both content of the current utterance and context (previous utterances) of dialogue and we present this work on two domains: human-human tutoring sessions and multi-party chat based intelligent tutoring systems. The proposed speech act classification models were trained and tested on chat utterances extracted from the tutoring sessions and based on the domain specific properties of the datasets were designed to work with hierarchical and granular speech act taxonomies.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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