  Many recent works on Entity Resolution (ER) leverage Deep Learning techniques
involving language models to improve effectiveness. This is applied to both
main steps of ER, i.e., blocking and matching. Several pre-trained embeddings
have been tested, with the most popular ones being fastText and variants of the
BERT model. However, there is no detailed analysis of their pros and cons. To
cover this gap, we perform a thorough experimental analysis of 12 popular
language models over 17 established benchmark datasets. First, we assess their
vectorization overhead for converting all input entities into dense embeddings
vectors. Second, we investigate their blocking performance, performing a
detailed scalability analysis, and comparing them with the state-of-the-art
deep learning-based blocking method. Third, we conclude with their relative
performance for both supervised and unsupervised matching. Our experimental
results provide novel insights into the strengths and weaknesses of the main
language models, facilitating researchers and practitioners to select the most
suitable ones in practice.
