Synthesizing extraction rules from user examples with SEER

Maeda F. Hanafi, Azza Abouzied, Laura Chiticariu, Yunyao Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Our demonstration showcases SEER's end-to-end Information Extraction (IE) workflow where users highlight texts they wish to extract. Given a small set of user-specified example extractions, SEER synthesizes easy-to-understand IE rules and suggests them to the user. In addition to rule suggestions, users can quickly pick the desired rule by filtering the rule suggestion by accepting or rejecting proposed extractions. SEER's workflow allows users to jump start the IE rule development cycle; it is a less time-consuming alternative to machine learning methods that require large labeled datasets or rule-based approaches that are labor-intensive. SEER's design principles and learning algorithm are motivated by how rule developers naturally construct data extraction rules.

Original languageEnglish (US)
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1687-1690
Number of pages4
ISBN (Electronic)9781450341974
DOIs
StatePublished - May 9 2017
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
VolumePart F127746
ISSN (Print)0730-8078

Other

Other2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
CountryUnited States
CityChicago
Period5/14/175/19/17

Keywords

  • Data extraction
  • Example-driven learning
  • Information extraction

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Hanafi, M. F., Abouzied, A., Chiticariu, L., & Li, Y. (2017). Synthesizing extraction rules from user examples with SEER. In SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1687-1690). (Proceedings of the ACM SIGMOD International Conference on Management of Data; Vol. Part F127746). Association for Computing Machinery. https://doi.org/10.1145/3035918.3056443