Classification-Aware Neural Topic Model Combined With Interpretable Analysis -- For Conflict Classification
This addresses the problem of analyzing conflict events for researchers or analysts, but appears incremental as it builds on existing neural topic models by adding interpretability and optimization.
The paper tackles conflict event analysis by proposing a Classification-Aware Neural Topic Model (CANTM-IA) that combines classification and topic discovery with interpretability, aiming to improve classification performance and provide reliable interpretations while reducing model complexity.
A large number of conflict events are affecting the world all the time. In order to analyse such conflict events effectively, this paper presents a Classification-Aware Neural Topic Model (CANTM-IA) for Conflict Information Classification and Topic Discovery. The model provides a reliable interpretation of classification results and discovered topics by introducing interpretability analysis. At the same time, interpretation is introduced into the model architecture to improve the classification performance of the model and to allow interpretation to focus further on the details of the data. Finally, the model architecture is optimised to reduce the complexity of the model.