Demo Data from EDBT 2026 / reviewers in the wild / expert
Peter-Arno Coppen
dblp:06/4420
· DBLP profile ↗
6ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4Databases, data management, data science and information retrieval · 2
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 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › question answering
answer extraction |
0.1 | 1 | 2007 | Evaluating discourse-based answer extraction for why-question answering · SIGIR 2007 |
Information retrieval
question answering and dialogue systems |
0.1 | 1 | 2007 | Evaluating discourse-based answer extraction for why-question answering · SIGIR 2007 |
Information retrieval › question answering
why-question answering |
0.1 | 1 | 2007 | Evaluating discourse-based answer extraction for why-question answering · SIGIR 2007 |
Methods — techniques the papers use, named apart from their topics
discourse analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | What Is Not in the Bag of Words for Why-QA?abstractWhile developing an approach to why-QA, we extended a passage retrieval system that uses off-the-shelf retrieval technology with a re-ranking step incorporating structural information. We get significantly higher scores in terms of MRR@150 (from 0.25 to 0.34) and success@10. The 23% improvement that we reach in terms of MRR is comparable to the improvement reached on different QA tasks by other researchers in the field, although our re-ranking approach is based on relatively lightweight overlap measures incorporating syntactic constituents, cue words, and document structure. Suzan Verberne, Lou Boves, Nelleke Oostdijk, Peter-Arno Coppen |
Comput. Linguistics | 4 |
| 2008 | Using Syntactic Information for Improving Why-Question Answering
Suzan Verberne, Lou Boves, Nelleke Oostdijk, Peter-Arno Coppen |
COLING | 4 |
| 2008 | Evaluating Paragraph Retrieval for
Suzan Verberne, Lou Boves, Nelleke Oostdijk, Peter-Arno Coppen |
ECIR | 4 |
| 2007 | Evaluating discourse-based answer extraction for why-question answeringabstractNo abstract available. Suzan Verberne, Lou Boves, Nelleke Oostdijk, Peter-Arno Coppen |
SIGIR | 4 |
| 2006 | Data for question answering: The case of why
Suzan Verberne, Lou Boves, Nelleke Oostdijk, Peter-Arno Coppen |
LREC | 4 |
| 2002 | Using Grammatical Description as a Metalanguage Resource
Folkert de Vriend, Peter-Arno Coppen, W. Haeseryn |
LREC | 2 |