VLDB 2026 Research / reviewers in the wild / expert
Hanyin Shao
dblp:306/0883
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
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 |
Language models and text generation · 77% Information extraction and text analysis · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › text generation › conditional text generation
definition generation |
0.6 | 1 | 2022 | Understanding Jargon: Combining Extraction and Generation for Definition Modeling · EMNLP 2022 |
Natural language and speech › Information extraction and text analysis › relation extraction
definition extraction |
0.2 | 1 | 2022 | Understanding Jargon: Combining Extraction and Generation for Definition Modeling · EMNLP 2022 |
Methods — techniques the papers use, named apart from their topics
generation · 0.6extraction · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Understanding Jargon: Combining Extraction and Generation for Definition ModelingabstractCan machines know what twin prime is?From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly what twin stands for without additional knowledge.Here, twin prime is a jargon-a specialized term used by experts in a particular field.Explaining jargon is challenging since it usually requires domain knowledge to understand.Recently, there is an increasing interest in extracting and generating definitions of words automatically.However, existing approaches, either extraction or generation, perform poorly on jargon.In this paper, we propose to combine extraction and generation for jargon definition modeling: first extract self-and correlative definitional information of target jargon from the Web and then generate the final definitions by incorporating the extracted definitional information.Our framework is remarkably simple but effective: experiments demonstrate our method can generate high-quality definitions for jargon and outperform state-of-the-art models significantly, e.g., BLEU score from 8.76 to 22.66 and human-annotated score from 2.34 to 4.04. 1 Jie Huang 0009, Hanyin Shao, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-Mei W. Hwu |
EMNLP | 2 |