Empirical estimates of adaptation: the chance of two noriegas is closer to p/2 than p2
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
STRUCTURAL SIMILARITIES OF COMPLEX NETWORKS: A COMPUTATIONAL MODEL BY EXAMPLE OF WIKI GRAPHS
Applied Artificial Intelligence
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Demonstrations Session
Computational modelling of structural priming in dialogue
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Robust automatic time alignment of orthographic transcriptions with unconstrained speech
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Computer Speech and Language
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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.