Demo Data from EDBT 2026 / reviewers in the wild / expert
David Ödling
dblp:169/3317
· 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 |
Information extraction and text analysis · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › distributional semantics
distributional semantic models |
0.2 | 1 | 2015 | Factorization of Latent Variables in Distributional Semantic Models · EMNLP 2015 |
Methods — techniques the papers use, named apart from their topics
weight redistribution · 0.2factorization techniques · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Factorization of Latent Variables in Distributional Semantic ModelsabstractThis paper discusses the use of factorization techniques in distributional semantic models.We focus on a method for redistributing the weight of latent variables, which has previously been shown to improve the performance of distributional semantic models.However, this result has not been replicated and remains poorly understood.We refine the method, and provide additional theoretical justification, as well as empirical results that demonstrate the viability of the proposed approach. Arvid Österlund, David Ödling, Magnus Sahlgren |
EMNLP | 2 |