Demo Data from EDBT 2026 / reviewers in the wild / expert
Clint Malcolm
dblp:169/3333
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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 |
Knowledge representation and reasoning · 50% 3D vision · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
geometric reasoning |
0.2 | 1 | 2015 | Solving Geometry Problems: Combining Text and Diagram Interpretation · EMNLP 2015 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › spatial reasoning
geometry problem solving |
0.2 | 1 | 2015 | Solving Geometry Problems: Combining Text and Diagram Interpretation · EMNLP 2015 |
Methods — techniques the papers use, named apart from their topics
submodular optimization · 0.2semantic parsing · 0.2dependency parsing · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Solving Geometry Problems: Combining Text and Diagram InterpretationabstractThis paper introduces GEOS, the first automated system to solve unaltered SAT geometry questions by combining text understanding and diagram interpretation.We model the problem of understanding geometry questions as submodular optimization, and identify a formal problem description likely to be compatible with both the question text and diagram.GEOS then feeds the description to a geometric solver that attempts to determine the correct answer.In our experiments, GEOS achieves a 49% score on official SAT questions, and a score of 61% on practice questions. 1 Finally, we show that by integrating textual and visual information, GEOS boosts the accuracy of dependency and semantic parsing of the question text. Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi, Oren Etzioni, Clint Malcolm |
EMNLP | 5 |