A Study of Document Expansion using Translation Models and Dimensionality Reduction Methods

Over a decade of research on document expansion methods resulted in several independent avenues, including smoothing methods, translation models, and dimensionality reduction techniques, such as matrix decompositions and topic models. Although these research avenues have been individually explored in many previous studies, there is still a lack of understanding of how state-of-the-art methods for each of these directions compare with each other in terms of retrieval accuracy. This paper eliminates this gap by reporting the results of an empirical comparison of document expansion methods using translation models estimated based on word co-occurrence and cosine similarity between low-dimensional word embeddings, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), on standard TREC collections. Experimental results indicate that LDA-based document expansion consistently outperforms both types of translation models and NMF according to all evaluation metrics for all and difficult queries, which is closely followed by translation model using word embeddings.