Topic Coverage

Topic coverage is a method for evaluating the performance of topic models by matching the topics generated by the model with reference topics discovered by humans and represented in a machine-readable format. This method simulates the topic modeling process within a fixed text corpus and reference topic setting, offering an evaluation approach based on real-world application scenarios. It can automatically validate the effectiveness of existing and future topic models on a large scale, thereby providing crucial support for model optimization and performance assessment in the field of natural language processing.