Demo Data from VLDB 2026 / reviewers in the wild / expert
Renée Pohlmann
dblp:26/2367
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1996
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › indexing
stemming |
0.0 | 1 | 1996 | Viewing Stemming as Recall Enhancement · SIGIR 1996 |
Information retrieval
retrieval evaluation |
0.0 | 1 | 1996 | Viewing Stemming as Recall Enhancement · SIGIR 1996 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1996 | Viewing Stemming as Recall EnhancementabstractPrevious research on stemming has shown both positive and negative effects on retrieval performance.This paper describes an experiment in which several linguistic and non-linguistic stemmers are evaluated on a Dutch test collection.Experiments especially focus on the measurement of Recall.Results show that linguistic stemming restricted to inflection yields a significant improvement over full linguistic and non-linguistic stemming, both in average Precision and R-Recall.Best results are obtained with a linguistic stemmer which is enhanced with compound analysis.This version has a significantly better Recall than a system without stemming, without a significant deterioration of Precision. Wessel Kraaij, Renée Pohlmann |
SIGIR | 2 |