Demo Data from VLDB 2026 / reviewers in the wild / expert
Aaron Fernandes
dblp:34/2286
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—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% | |
| Artificial intelligence
1 paper |
Question answering and dialogue systems · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Question answering and dialogue systems
passage retrieval |
0.0 | 1 | 2003 | Quantitative evaluation of passage retrieval algorithms for question answering · SIGIR 2003 |
Information retrieval › document retrieval
passage retrieval |
0.0 | 1 | 2003 | Quantitative evaluation of passage retrieval algorithms for question answering · SIGIR 2003 |
Information retrieval
evaluation |
0.0 | 1 | 2003 | Quantitative evaluation of passage retrieval algorithms for question answering · SIGIR 2003 |
Methods — techniques the papers use, named apart from their topics
density-based scoring · 0.1boolean querying · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Quantitative evaluation of passage retrieval algorithms for question answeringabstractPassage retrieval is an important component common to many question answering systems. Because most evaluations of question answering systems focus on end-to-end performance, comparison of common components becomes di#cult. To address this shortcoming, we present a quantitative evaluation of various passage retrieval algorithms for question answering, implemented in a framework called Pauchok. We present three important findings: Boolean querying schemes perform well in the question answering task. The performance di#erences between various passage retrieval algorithms vary with the choice of document retriever, which suggests significant interactions between document retrieval and passage retrieval. The best algorithms in our evaluation employ density-based measures for scoring query terms. Our results reveal future directions for passage retrieval and question answering. Stefanie Tellex, Boris Katz, Jimmy Lin, Aaron Fernandes, Gregory Marton |
SIGIR | 4 |