@inproceedings{b89d2cfac8d3434fbc30713875b4f84b,
title = "Neural extractive search",
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.",
author = "Shauli Ravfogel and Hillel Taub-Tabib and Yoav Goldberg",
note = "Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics; Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstration, ACL-IJCNLP 2021 ; Conference date: 01-08-2021 Through 06-08-2021",
year = "2021",
month = jan,
day = "1",
doi = "10.18653/v1/2021.acl-demo.25",
language = "الإنجليزيّة",
series = "ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the System Demonstrations",
publisher = "Association for Computational Linguistics (ACL)",
pages = "210--217",
booktitle = "ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the System Demonstrations",
address = "الولايات المتّحدة",
}