TY - GEN
T1 - Synthesizing extraction rules from user examples with SEER
AU - Hanafi, Maeda F.
AU - Abouzied, Azza
AU - Chiticariu, Laura
AU - Li, Yunyao
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/5/9
Y1 - 2017/5/9
N2 - 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.
AB - 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.
KW - Data extraction
KW - Example-driven learning
KW - Information extraction
UR - http://www.scopus.com/inward/record.url?scp=85021219818&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021219818&partnerID=8YFLogxK
U2 - 10.1145/3035918.3056443
DO - 10.1145/3035918.3056443
M3 - Conference contribution
AN - SCOPUS:85021219818
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 1687
EP - 1690
BT - SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PB - Association for Computing Machinery
T2 - 2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
Y2 - 14 May 2017 through 19 May 2017
ER -