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        <title>Neural Extractive Search</title>
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    <abstract>Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called “extractive search”, in which a search query is enriched with capture-slots, to allow for such rapid extraction. Such an extractive search system can be built around syntactic structures, resulting in high-precision, low-recall results. We show how the recall can be improved using neural retrieval and alignment. The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Our prototype is available at https://spike.neural-sim.apps.allenai.org/ and a video demonstration is available at https://vimeo.com/559586687.</abstract>
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%0 Conference Proceedings
%T Neural Extractive Search
%A Ravfogel, Shauli
%A Taub-Tabib, Hillel
%A Goldberg, Yoav
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F ravfogel-etal-2021-neural
%X Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called “extractive search”, in which a search query is enriched with capture-slots, to allow for such rapid extraction. Such an extractive search system can be built around syntactic structures, resulting in high-precision, low-recall results. We show how the recall can be improved using neural retrieval and alignment. The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Our prototype is available at https://spike.neural-sim.apps.allenai.org/ and a video demonstration is available at https://vimeo.com/559586687.
%R 10.18653/v1/2021.acl-demo.25
%U https://aclanthology.org/2021.acl-demo.25
%U https://doi.org/10.18653/v1/2021.acl-demo.25
%P 210-217

Markdown (Informal)

[Neural Extractive Search](https://aclanthology.org/2021.acl-demo.25) (Ravfogel et al., ACL-IJCNLP 2021)

• Neural Extractive Search (Ravfogel et al., ACL-IJCNLP 2021)

ACL

• Shauli Ravfogel, Hillel Taub-Tabib, and Yoav Goldberg. 2021. Neural Extractive Search. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 210–217, Online. Association for Computational Linguistics.