TY - GEN
T1 - SEER
T2 - 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
AU - Hanafi, Maeda F.
AU - Abouzied, Azza
AU - Chiticariu, Laura
AU - Li, Yunyao
N1 - Funding Information:
Acknowledgments We acknowledge the generous support of the U.S. National Science Foundation under award IIS-1420941.
Publisher Copyright:
© 2017 ACM.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - Time-consuming and complicated best describe the current state of the Information Extraction (IE) field. Machine learning approaches to IE require large collections of labeled datasets that are difficult to create and use obscure mathematical models, occasionally returning unwanted results that are unexplainable. Rule-based approaches, while resulting in easy-to-understand IE rules, are still time-consuming and labor-intensive. SEER combines the best of these two approaches: a learning model for IE rules based on a small number of user-specified examples. In this paper, we explain the design behind SEER and present a user study comparing our system against a commercially available tool in which users create IE rules manually. Our results show that SEER helps users complete text extraction tasks more quickly, as well as more accurately.
AB - Time-consuming and complicated best describe the current state of the Information Extraction (IE) field. Machine learning approaches to IE require large collections of labeled datasets that are difficult to create and use obscure mathematical models, occasionally returning unwanted results that are unexplainable. Rule-based approaches, while resulting in easy-to-understand IE rules, are still time-consuming and labor-intensive. SEER combines the best of these two approaches: a learning model for IE rules based on a small number of user-specified examples. In this paper, we explain the design behind SEER and present a user study comparing our system against a commercially available tool in which users create IE rules manually. Our results show that SEER helps users complete text extraction tasks more quickly, as well as more accurately.
KW - Data extraction
KW - Example-driven learning
UR - http://www.scopus.com/inward/record.url?scp=85021225025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021225025&partnerID=8YFLogxK
U2 - 10.1145/3025453.3025540
DO - 10.1145/3025453.3025540
M3 - Conference contribution
AN - SCOPUS:85021225025
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 6672
EP - 6682
BT - CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 6 May 2017 through 11 May 2017
ER -