Demo Data from EDBT 2026 / reviewers in the wild / expert
Joseph Smarr
dblp:90/1519
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › web information extraction
wrapper induction |
0.0 | 1 | 2004 | Sources of Success for Boosted Wrapper Induction · J. Mach. Learn. Res. 2004 |
Data mining › text mining
information extraction |
0.0 | 1 | 2004 | Sources of Success for Boosted Wrapper Induction · J. Mach. Learn. Res. 2004 |
Data mining
text mining |
0.0 | 1 | 2004 | Sources of Success for Boosted Wrapper Induction · J. Mach. Learn. Res. 2004 |
Methods — techniques the papers use, named apart from their topics
boosting · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Sources of Success for Boosted Wrapper Induction
David Kauchak, Joseph Smarr, Charles Elkan |
J. Mach. Learn. Res. | 2 |
| 2003 | Named Entity Recognition with Character-Level Models
Daniel Klein 0001, Joseph Smarr, Christopher D. Manning |
CoNLL | 2 |