Enhancing Topic Modeling Through Embedding Learning Strategies

Abstract

In the field of Natural Language Processing (NLP), topic modeling is crucial for uncovering patterns in textual data. Recent advances have combined traditional topic modeling with word embeddings, introducing the Embedded Topic Model (ETM). This thesis explores embedding learning strategies within topic modeling to improve the ETM and related models. It delves into more efficient variational inference, advanced word embedding techniques, and strategies for better topic interpretability. Practical implications in document classification, content recommendation, and summarization are evaluated. Scalability challenges for handling large textual corpora are also addressed. The integration of textual data with other modalities is pioneered. This work aims to enhance topic modeling using embedding learning strategies, bridging the gap between theory and practice in NLP.