Topic-time Heatmaps for Human-in-the-loop Topic Detection and Tracking
This addresses the need for human guidance in defining event scopes for TDT in applications like search engines, but it appears incremental as it builds on existing TDT methods with user interaction.
The paper tackles the problem of fine-tuning Topic Detection and Tracking (TDT) algorithms for organizing news media into event clusters by introducing a human-in-the-loop method that uses visual overviews and interactive questions to improve event similarity models, though no concrete results or numbers are reported as it is a work in progress.
The essential task of Topic Detection and Tracking (TDT) is to organize a collection of news media into clusters of stories that pertain to the same real-world event. To apply TDT models to practical applications such as search engines and discovery tools, human guidance is needed to pin down the scope of an "event" for the corpus of interest. In this work in progress, we explore a human-in-the-loop method that helps users iteratively fine-tune TDT algorithms so that both the algorithms and the users themselves better understand the nature of the events. We generate a visual overview of the entire corpus, allowing the user to select regions of interest from the overview, and then ask a series of questions to affirm (or reject) that the selected documents belong to the same event. The answers to these questions supplement the training data for the event similarity model that underlies the system.