Demo Data from VLDB 2026 / reviewers in the wild / expert
Tobias Kaufmann 0004
dblp:16/3424-4
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2017
0000-0002-4003-1018ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 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
2 papers |
Information extraction and text analysis · 39% Language models and text generation · 26% Question answering and dialogue systems · 26% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › pattern learning
template learning |
0.3 | 1 | 2017 | Template Induction over Unstructured Email Corpora · WWW 2017 |
Natural language and speech › Question answering and dialogue systems › dialogue generation
dialogue response generation |
0.2 | 1 | 2016 | Smart Reply: Automated Response Suggestion for Email · KDD 2016 |
Natural language and speech › Language models and text generation
text generation |
0.2 | 1 | 2016 | Smart Reply: Automated Response Suggestion for Email · KDD 2016 |
Natural language and speech › Information extraction and text analysis
text classification |
0.1 | 1 | 2017 | Template Induction over Unstructured Email Corpora · WWW 2017 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
large-scale inference |
0.1 | 1 | 2016 | Smart Reply: Automated Response Suggestion for Email · KDD 2016 |
Methods — techniques the papers use, named apart from their topics
unsupervised template induction · 0.3sequence-to-sequence generation · 0.2deep learning · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Template Induction over Unstructured Email CorporaabstractUnsupervised template induction over email data is a central component in applications such as information extraction, document classification, and auto-reply. The benefits of automatically generating such templates are known for structured data, e.g. machine generated HTML emails. However much less work has been done in performing the same task over unstructured email data. Julia Proskurnia, Marc-Allen Cartright, Lluís Garcia Pueyo, Ivo Krka, James B. Wendt, Tobias Kaufmann 0004, Balint Miklos |
WWW | 6 |
| 2016 | Smart Reply: Automated Response Suggestion for EmailabstractIn this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply. It generates semantically diverse suggestions that can be used as complete email responses with just one tap on mobile. The system is currently used in Inbox by Gmail and is responsible for assisting with 10% of all mobile responses. It is designed to work at very high throughput and process hundreds of millions of messages daily. The system exploits state-of-the-art, large-scale deep learning. Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann 0004, Andrew Tomkins, Balint Miklos, Gregory S. Corrado, László Lukács, Marina Ganea, Vivek Ramavajjala |
KDD | 4 |