@inproceedings{2fd38057badc4b80a5a155ddc6faae63,
title = "SEER: Auto-generating information extraction rules from user-specified examples",
abstract = "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.",
keywords = "Data extraction, Example-driven learning",
author = "Hanafi, {Maeda F.} and Azza Abouzied and Laura Chiticariu and Yunyao Li",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 ; Conference date: 06-05-2017 Through 11-05-2017",
year = "2017",
month = may,
day = "2",
doi = "10.1145/3025453.3025540",
language = "English (US)",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "6672--6682",
booktitle = "CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems",
}