Topic-time Heatmaps for Human-in-the-loop Topic Detection and Tracking
arXiv:2110.07337
Abstract
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.
arXiv commentAccepted to DaSH Workshop, KDD 2021
References in corpus (6)
- In Defense of the Triplet Loss for Person Re-Identification
- Time2Vec: Learning a Vector Representation of Time
- ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
- Putting Humans in the Natural Language Processing Loop: A Survey
- Multilingual Clustering of Streaming News
- Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models