Title

A machine learning approach to pronominal anaphora resolution in dialogue based intelligent tutoring systems

Abstract

Anaphora resolution is a central topic in dialogue and discourse that deals with finding the referent of a pronoun. It plays a critical role in conversational Intelligent Tutoring Systems (ITSs) as it can increase the accuracy of assessing students' knowledge level, i.e. mental model, based on their natural language inputs. Although anaphora resolution is one of the most studied problems in Natural Language Processing, there are very few studies that focus on anaphora resolution in dialogue based ITSs. To this end, we present Deep Anaphora Resolution Engine++ (DARE++) that adapts and extends existing machine learning solutions to resolve pronouns in ITS dialogues. Experiments showed that DARE++ achieves a F-measure of 88.93%, proving the potential of the proposed method for resolving pronouns in student-tutor dialogues. © 2014 Springer-Verlag Berlin Heidelberg.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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