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    <abstract>Fundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations arise in complex ways that cannot be captured using textual similarity metrics. However, entities have underlying structures, typically shared by entities of the same entity type, that can help reason over their name variations. Discovering, learning and manipulating these structures typically requires high manual effort in the form of large amounts of labeled training data and handwritten transformation programs. In this work, we propose an active-learning based framework that drastically reduces the labeled data required to learn the structures of entities. We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels. Our experiments show that our framework consistently outperforms both handwritten programs and supervised learning models. We also demonstrate the utility of our framework in relation extraction and entity resolution tasks.</abstract>
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%0 Conference Proceedings
%T Exploiting Structure in Representation of Named Entities using Active Learning
%A Bhutani, Nikita
%A Qian, Kun
%A Li, Yunyao
%A Jagadish, H. V.
%A Hernandez, Mauricio
%A Vasa, Mitesh
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F bhutani-etal-2018-exploiting
%X Fundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations arise in complex ways that cannot be captured using textual similarity metrics. However, entities have underlying structures, typically shared by entities of the same entity type, that can help reason over their name variations. Discovering, learning and manipulating these structures typically requires high manual effort in the form of large amounts of labeled training data and handwritten transformation programs. In this work, we propose an active-learning based framework that drastically reduces the labeled data required to learn the structures of entities. We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels. Our experiments show that our framework consistently outperforms both handwritten programs and supervised learning models. We also demonstrate the utility of our framework in relation extraction and entity resolution tasks.
%U https://aclanthology.org/C18-1058
%P 687-699

Markdown (Informal)

[Exploiting Structure in Representation of Named Entities using Active Learning](https://aclanthology.org/C18-1058) (Bhutani et al., COLING 2018)

• Exploiting Structure in Representation of Named Entities using Active Learning (Bhutani et al., COLING 2018)

ACL

• Nikita Bhutani, Kun Qian, Yunyao Li, H. V. Jagadish, Mauricio Hernandez, and Mitesh Vasa. 2018. Exploiting Structure in Representation of Named Entities using Active Learning. In Proceedings of the 27th International Conference on Computational Linguistics, pages 687–699, Santa Fe, New Mexico, USA. Association for Computational Linguistics.