ChatGrape
Kommunikationsplattform zur Vernetzung von Wissen und Workflows in Unternehmen

In the project, an approach was developed and implemented 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.

• Schabus, D., Krenn, B., Neubarth, F. (2016). Data-Driven Identification of Dialogue Acts in Chat Messages. Proceedings of the 13th Conference on Natural Language Processing (KONVENS). Bochum, Germany.

Research staff

• Brigitte Krenn
• Dietmar Schabus
• Friedrich Neubarth

Partners

• ChatGrape

Sponsor