Harvesting Wikipedia Knowledge to Identify Topics in Ongoing Natural Language Dialogs

This paper introduces a model harvesting the crowd-sourced encyclopedic knowledge provided by Wikipedia to improve the conversational abilities of an artificial agent. More precisely, we present a model for automatic topic identification in ongoing natural language dialogs. On the basis of a graph-based representation of the Wikipedia category system, our model implements six tasks essential for detecting the topical overlap of coherent dialog contributions. Thereby the identification process operates online to handle dialog streams of constantly changing topical threads in real-time. The realization of the model and its application to our conversational agent aims to improve human-agent conversations by transferring human-like topic awareness to the artificial interlocutor.