  Locality-sensitive hashing (LSH) is a fundamental algorithmic technique
widely employed in large-scale data processing applications, such as
nearest-neighbor search, entity resolution, and clustering. However, its
applicability in some real-world scenarios is limited due to the need for
careful design of hashing functions that align with specific metrics. Existing
LSH-based Entity Blocking solutions primarily rely on generic similarity
metrics such as Jaccard similarity, whereas practical use cases often demand
complex and customized similarity rules surpassing the capabilities of generic
similarity metrics. Consequently, designing LSH functions for these customized
similarity rules presents considerable challenges. In this research, we propose
a neuralization approach to enhance locality-sensitive hashing by training deep
neural networks to serve as hashing functions for complex metrics. We assess
the effectiveness of this approach within the context of the entity resolution
problem, which frequently involves the use of task-specific metrics in
real-world applications. Specifically, we introduce NLSHBlock (Neural-LSH
Block), a novel blocking methodology that leverages pre-trained language
models, fine-tuned with a novel LSH-based loss function. Through extensive
evaluations conducted on a diverse range of real-world datasets, we demonstrate
the superiority of NLSHBlock over existing methods, exhibiting significant
performance improvements. Furthermore, we showcase the efficacy of NLSHBlock in
enhancing the performance of the entity matching phase, particularly within the
semi-supervised setting.
