Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3035918.3056443 | SIGMOD Conference |
Keywords | Field | DocType |
Data Extraction,Example-driven learning,Information Extraction | Design elements and principles,Data mining,Computer science,Jump start,Filter (signal processing),Information extraction,Data extraction,Small set,Workflow,Database | Conference |
Citations | PageRank | References |
3 | 0.36 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maeda F. Hanafi | 1 | 4 | 1.40 |
| Azza Abouzied | 2 | 24 | 3.18 |
| Laura Chiticariu | 3 | 10 | 1.51 |
| Yunyao Li | 4 | 530 | 37.81 |