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        <name type="personal">
            <namePart type="given">Kalika</namePart>
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    <abstract>Many real-world NLP applications face the challenge of training an entity disambiguation model for a specific domain with a small labeling budget. In this setting there is often access to a large unlabeled pool of documents. It is then natural to ask the question: which samples should be selected for annotation? In this paper we propose a solution that combines feature diversity with low rank correction. Our sampling strategy is formulated in the context of bilinear tensor models. Our experiments show that the proposed approach can significantly reduce the amount of labeled data necessary to achieve a given performance.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.479</identifier>
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%0 Conference Proceedings
%T Entity Disambiguation on a Tight Labeling Budget
%A Primadhanty, Audi
%A Quattoni, Ariadna
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F primadhanty-quattoni-2023-entity
%X Many real-world NLP applications face the challenge of training an entity disambiguation model for a specific domain with a small labeling budget. In this setting there is often access to a large unlabeled pool of documents. It is then natural to ask the question: which samples should be selected for annotation? In this paper we propose a solution that combines feature diversity with low rank correction. Our sampling strategy is formulated in the context of bilinear tensor models. Our experiments show that the proposed approach can significantly reduce the amount of labeled data necessary to achieve a given performance.
%R 10.18653/v1/2023.findings-emnlp.479
%U https://aclanthology.org/2023.findings-emnlp.479
%U https://doi.org/10.18653/v1/2023.findings-emnlp.479
%P 7208-7215

Markdown (Informal)

[Entity Disambiguation on a Tight Labeling Budget](https://aclanthology.org/2023.findings-emnlp.479) (Primadhanty & Quattoni, Findings 2023)

• Entity Disambiguation on a Tight Labeling Budget (Primadhanty & Quattoni, Findings 2023)

ACL

• Audi Primadhanty and Ariadna Quattoni. 2023. Entity Disambiguation on a Tight Labeling Budget. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7208–7215, Singapore. Association for Computational Linguistics.