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.- Anthology ID:
- 2021.acl-demo.25
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 210–217
- Language:
- URL:
- https://aclanthology.org/2021.acl-demo.25
- DOI:
- 10.18653/v1/2021.acl-demo.25
- Cite (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.
- Cite (Informal):
- Neural Extractive Search (Ravfogel et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.acl-demo.25.pdf