  Entity standardization maps noisy mentions from free-form text to standard
entities in a knowledge base. The unique challenge of this task relative to
other entity-related tasks is the lack of surrounding context and numerous
variations in the surface form of the mentions, especially when it comes to
generalization across domains where labeled data is scarce. Previous research
mostly focuses on developing models either heavily relying on context, or
dedicated solely to a specific domain. In contrast, we propose CoSiNES, a
generic and adaptable framework with Contrastive Siamese Network for Entity
Standardization that effectively adapts a pretrained language model to capture
the syntax and semantics of the entities in a new domain.
  We construct a new dataset in the technology domain, which contains 640
technical stack entities and 6,412 mentions collected from industrial content
management systems. We demonstrate that CoSiNES yields higher accuracy and
faster runtime than baselines derived from leading methods in this domain.
CoSiNES also achieves competitive performance in four standard datasets from
the chemistry, medicine, and biomedical domains, demonstrating its cross-domain
applicability.
