Abstract
This paper proposes a computational model for analyzing the communicative structure of computer-mediated chat dialogues, reporting the present results of our empirical evaluation.We first formalize communicative structure underlying chat dialogues by decomposing it into continuation relations and response relations.A continuation relation holds between utterences of the same speaker that constitute a complete chunk functioning as a question response, etc.(e.g. the relation between the separate utterences Are and you a student?, which constitute a question).A response relations, on the other hand, holds between utterances, e.g.a question and its response, made by different speakers.Our model analyzes communicative structure by grouping utterances together according to these types of relations in a bottom-up fashion. For this process, we use corpus-based supervised machine learning. We manually annotated a chat dialogue corpus with communicative structure (two-person and three-person dialogues: 69 dialogues in total, containing 11, 905 utterance tokens).The automatic analyses matched the manual analyses in 87.4% for two-person dialogues and 84.6% for three.
