Demo Data from EDBT 2026 / reviewers in the wild / expert
Helena Prepens
dblp:245/8678
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—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 2 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 › lexical semantics › verb semantics
aspectual classification |
0.4 | 1 | 2019 | Annotation and Automatic Classification of Aspectual Categories · ACL (1) 2019 |
Natural language and speech › Information extraction and text analysis
lexical semantics |
0.1 | 1 | 2019 | Annotation and Automatic Classification of Aspectual Categories · ACL (1) 2019 |
Methods — techniques the papers use, named apart from their topics
supervised classification · 0.4corpus annotation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Annotation and Automatic Classification of Aspectual CategoriesabstractWe present the first annotated resource for the aspectual classification of German verb tokens in their clausal context. We use aspectual features compatible with the plurality of aspectual classifications in previous work and treat aspectual ambiguity systematically. We evaluate our corpus by using it to train supervised classifiers to automatically assign aspectual categories to verbs in context, permitting favourable comparisons to previous work. Markus Egg, Helena Prepens, Will Roberts |
ACL (1) | 2 |