SEER: Auto-Generating Information Extraction Rules from User-Specified Examples
Abstract
SEER is an 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.
Publications
Research Paper, CHI 2017
Maeda F. Hanafi, Azza Abouzied, Laura Chiticariu, and Yunyao Li - CHI'2017
Demo Poster, SIGMOD 2017
Demo Paper, SIGMOD 2017
Maeda F. Hanafi, Azza Abouzied, Laura Chiticariu, and Yunyao Li - CHI'2017
Videos and Slides
Repository Content
This repository contains links to items related to SEER. The project source code is private as it contains proprietary code.