Combining Topic Models for Corpus Exploration
Carsten Schnober, Iryna Gurevych · 2015
We investigate new ways of applying LDA topic models: rather than optimizing a single model for a specific use case, we train multiple models based on different parameters and vocabularies which are combined on-the-fly to comply with varying information retrieval tasks. We also show a semi-automatic method which helps users to identify relevant topics across multiple models.