Data-Driven Identification of Dialogue Acts in Chat Messages

by Dietmar Schabus, Brigitte Krenn, Friedrich Neubarth
Abstract:
We present an approach to classify chat messages into dialogue acts, focusing on questions and directives (“to-dos”). Our multi-lingual system uses word lexica, a specialized tokenizer and rule-based shallow syntactic analysis to compute relevant features, and then trains statistical models (support vector machines, random forests, etc.) for dialogue act prediction. The classification scores we achieve are very satisfactory on question detection and promising on to-do detection, on English and German data collections.
Reference:
Dietmar Schabus, Brigitte Krenn, Friedrich Neubarth, “Data-Driven Identification of Dialogue Acts in Chat Messages”, In Proceedings of the 13th Conference on Natural Language Processing (KONVENS), Bochum, Germany, pp. 236-241, 2016.
Bibtex Entry:
@InProceedings{Schabus2016,
  Title                    = {Data-Driven Identification of Dialogue Acts in Chat Messages},
  Author                   = {Dietmar Schabus and Brigitte Krenn and Friedrich Neubarth},
  Booktitle                = {Proceedings of the 13th Conference on Natural Language Processing (KONVENS)},
  Year                     = {2016},

  Address                  = {Bochum, Germany},
  Month                    = sep,
  Pages                    = {236-241},

  Abstract                 = {We present an approach to classify chat messages into dialogue acts, focusing on questions and directives ("to-dos"). Our multi-lingual system uses word lexica, a specialized tokenizer and rule-based shallow syntactic analysis to compute relevant features, and then trains statistical models (support vector machines, random forests, etc.) for dialogue act prediction. The classification scores we achieve are very satisfactory on question detection and promising on to-do detection, on English and German data collections.},
  Url                      = {https://www.linguistics.rub.de/konvens16/pub/30_konvensproc.pdf}
}