Demo Data from VLDB 2026 / reviewers in the wild / expert
Chia-Ling Lee
dblp:21/7468
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational social science and digital humanities
computational linguistics |
0.2 | 1 | 2014 | Semantical Clustering of Morphologically Related Chinese Words · AAAI 2014 |
Data mining › clustering
document clustering |
0.2 | 1 | 2014 | Semantical Clustering of Morphologically Related Chinese Words · AAAI 2014 |
Methods — techniques the papers use, named apart from their topics
linguistic features · 0.4computational linguistics · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Semantical Clustering of Morphologically Related Chinese WordsabstractA Chinese character embedded in different compound words may carry different meanings. In this paper, we aim at semantical clustering of a given family of morphologically related Chinese words. In Experiment 1, we employed linguistic features at the word, syntactic, semantic, and contextual levels in aggregated computational linguistics methods to handle the clustering task. In Experiment 2, we recruited adults and children to perform the clustering task. Experimental results indicate that our computational model achieved a similar level of performance as children. Chia-Ling Lee, Ya-Ning Chang, Chao-Lin Liu, Chia-Ying Lee, Yung-Jen Hsu 0001 |
AAAI | 1 |
| 2014 | Unsupervised Clustering of Morphologically Related Chinese Words
Chia-Ling Lee, Ya-Ning Chang, Chao-Lin Liu, Chia-Ying Lee, Yung-Jen Hsu 0001 |
CogSci | 1 |