Demo Data from EDBT 2026 / reviewers in the wild / expert
Shweta Ghonge
dblp:136/9209
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
—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 |
Information extraction and text analysis · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
pointwise mutual information |
0.2 | 1 | 2013 | Appropriately Incorporating Statistical Significance in PMI · EMNLP 2013 |
Methods — techniques the papers use, named apart from their topics
statistical significance testing · 0.2PMI variants · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Appropriately Incorporating Statistical Significance in PMIabstractTwo recent measures incorporate the notion of statistical significance in basic PMI formulation.In some tasks, we find that the new measures perform worse than the PMI.Our analysis shows that while the basic ideas in incorporating statistical significance in PMI are reasonable, they have been applied slightly inappropriately.By fixing this, we get new measures that improve performance over not just PMI but on other popular co-occurrence measures as well.In fact, the revised measures perform reasonably well compared with more resource intensive non co-occurrence based methods also. Om P. Damani, Shweta Ghonge |
EMNLP | 2 |