Related papers: Neural Extractive Search

Neural Extractive Search

URL: http://arxiv.org/abs/2106.04612v1
Date: Tue, 8 Jun 2021 18:03:31 GMT
Title: Neural Extractive Search
Authors: Shauli Ravfogel, Hillel Taub-Tabib, Yoav Goldberg
Abstract summary: 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. We show how the recall can be improved using neural retrieval and alignment.
Score: 53.15076679818303
License: http://creativecommons.org/licenses/by/4.0/
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 \url{https://spike.neural-sim.apps.allenai.org/} and a video demonstration is available at \url{https://vimeo.com/559586687}.

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