Demo Data from EDBT 2026 / reviewers in the wild / expert
Jorge López-Castro
dblp:46/8313
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval
search engines |
0.1 | 1 | 2010 | Blog snippets: a comments-biased approach · SIGIR 2010 |
Information retrieval › text summarization › extractive summarization
sentence selection |
0.1 | 1 | 2010 | Blog snippets: a comments-biased approach · SIGIR 2010 |
Information retrieval › text summarization
snippet generation |
0.1 | 1 | 2010 | Blog snippets: a comments-biased approach · SIGIR 2010 |
Methods — techniques the papers use, named apart from their topics
TREC methodology · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Blog snippets: a comments-biased approachabstractIn the last years Blog Search has been a new exciting task in Information Retrieval. The presence of user generated information with valuable opinions makes this field of huge interest. In this poster we use part of this information, the readers' comments, to improve the quality of post snippets with the objective of enhancing the user access to the relevant posts in a result list. We propose a simple method for snippet generation based on sentence selection, using the comments to guide the selection process. We evaluated our approach with standard TREC methodology in the Blogs06 collection showing significant improvements up to 32% in terms of MAP over the baseline. Javier Parapar, Jorge López-Castro, Álvaro Barreiro |
SIGIR | 2 |