Learning Folksonomies for Trend Detection in Task-Oriented Dialogues

Dialogues are created by the interaction between people, who speak different kinds of topics using natural language. Task-oriented dialogue aims the solution of a given task in a given domain. Folksonomies are knowledge structures composed of users, tags and resources. Folksonomies emerge from the tagging process in collaborative tagging systems. Dialogues and folksonomies have in common their social dimension. One of the main characteristics of the folksonomies is its social dimension (users), which is also presented in dialogues, through the interaction between human beings. In this research, we describe a method that performs the learning of folksonomies, represented by a quadripartite model, from task-oriented dialogues. Using the learned folksonomies, we propose an approach for trend detection (those topics being discussed more than others). The main difference from others approaches is that we use the content of each resource in this process. This can be useful for instance, to retrieve the topics addressed by the interlocutors of the dialogues, in different time intervals. Experiments with a real-world task-oriented dialogue corpus were done.