%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