@inproceedings{yuan-etal-2023-cosines,
    title = "{C}o{S}i{NES}: Contrastive {S}iamese Network for Entity Standardization",
    author = "Yuan, Jiaqing  and
      Merler, Michele  and
      Choudhury, Mihir  and
      Pavuluri, Raju  and
      Singh, Munindar  and
      Vukovic, Maja",
    editor = "Hruschka, Estevam  and
      Mitchell, Tom  and
      Rahman, Sajjadur  and
      Mladeni{\'c}, Dunja  and
      Grobelnik, Marko",
    booktitle = "Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, ON, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.matching-1.9",
    doi = "10.18653/v1/2023.matching-1.9",
    pages = "109--119",
    abstract = "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. Code and data is available at \url{https://github.com/konveyor/tackle-container-advisor/tree/main/entity_standardizer/cosines}",
}

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. Code and data is available at \url{https://github.com/konveyor/tackle-container-advisor/tree/main/entity_standardizer/cosines}
Yuan, Jiaqing Merler, Michele Choudhury, Mihir Pavuluri, Raju Singh, Munindar Vukovic, Maja
CoSiNES: Contrastive Siamese Network for Entity Standardization