Syntactic search by example

Micah Shlain, Hillel Taub-Tabib, Shoval Sadde, Yoav Goldberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

We present a system that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs. In contrast to previous attempts to this effect, we introduce a light-weight query language that does not require the user to know the details of the underlying syntactic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Search is performed at an interactive speed due to an efficient linguistic graph-indexing and retrieval engine. This allows for rapid exploration, development and refinement of syntax-based queries. We demonstrate the system using queries over two corpora: the English wikipedia, and a collection of English pubmed abstracts. A demo of the wikipedia system is avilable at: https://allenai.github.io/spike/ .

Original languageEnglish
Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the System Demonstrations
PublisherAssociation for Computational Linguistics (ACL)
Pages17-23
Number of pages7
ISBN (Electronic)9781952148040
StatePublished - 2020
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: 5 Jul 202010 Jul 2020

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online
Period5/07/2010/07/20

Bibliographical note

Publisher Copyright:
© 2020 Association for Computational Linguistics

Funding

This project has received funding from the Eu-ropoean Research Council (ERC) under the Eu-ropoean Union’s Horizon 2020 research and innovation programme, grant agreement No. 802774 (iEXTRACT). This project has received funding from the Europoean Research Council (ERC) under the Europoean Union?s Horizon 2020 research and innovation programme, grant agreement No. 802774 (iEXTRACT).

FundersFunder number
Eu-ropoean Research Council
Europoean Union?s Horizon 2020 research and innovation programme
Horizon 2020 Framework Programme
European Commission
Horizon 2020802774

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