VLDB 2026 Research / reviewers in the wild / expert
Yemin Luo
dblp:17/4805
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—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.
| Artificial intelligence
1 paper |
Question answering and dialogue systems · 33% Information extraction and text analysis · 33% Learning theory · 33% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › classification › nonlinear classification
kernel-based classification |
0.1 | 1 | 2008 | Question classification with semantic tree kernel · SIGIR 2008 |
Natural language and speech › Question answering and dialogue systems › question understanding
question classification |
0.1 | 1 | 2008 | Question classification with semantic tree kernel · SIGIR 2008 |
Natural language and speech › Information extraction and text analysis
text classification |
0.1 | 1 | 2008 | Question classification with semantic tree kernel · SIGIR 2008 |
Methods — techniques the papers use, named apart from their topics
support vector machine · 0.1semantic tree kernels · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Question classification with semantic tree kernelabstractQuestion Classification plays an important role in most Question Answering systems. In this paper, we exploit semantic features in Support Vector Machines (SVMs) for Question Classification. We propose a semantic tree kernel to incorporate semantic similarity information. A diverse set of semantic features is evaluated. Experimental results show that SVMs with semantic features, especially semantic classes, can significantly outperform the state-of-the-art systems. Yan Pan 0002, Yong Tang 0001, Luxian Lin, Yemin Luo |
SIGIR | 4 |