Mod­el­ling Lex­i­cal Align­ment in Spon­ta­neous Direc­tion Dia­logue Data by Means of a Lex­i­con Net­work Model

We apply a network model of lexical alignment, called Two-Level Time-Aligned Network Series, to natural route direction dialogue data. The model accounts for the structural similarity of interlocutors’ dialogue lexica. As classification criterion the directions are divided into effective and ineffective ones. We found that effective direction dialogues can be separated from ineffective ones with a hit ratio of 96% with regard to the structure of the corresponding dialogue lexica. This value is achieved when taking into account just nouns. This hit ratio decreases slightly as soon as other parts of speech are also considered. Thus, this paper provides a machine learning framework for telling apart effective dialogues from insufficient ones. It also implements first steps in more fine-grained alignment studies: we found a difference in the efficiency contribution between (the interaction of) lemmata of different parts of speech.