Demo Data from VLDB 2026 / reviewers in the wild / expert
Michael Ringgaard
dblp:71/8159
· DBLP profile ↗
6ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0003-3422-999XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 6
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
3 papers |
Information extraction and text analysis · 45% Kernel, tree and ensemble methods · 42% Optimization for machine learning · 7% | |
| Databases, data mining, and information retrieval
1 paper |
Data integration and cleaning · 100% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data integration and cleaning › entity resolution
collective entity resolution |
0.2 | 1 | 2016 | Collective Entity Resolution with Multi-Focal Attention · ACL (1) 2016 |
Data integration and cleaning
entity resolution |
0.2 | 1 | 2016 | Collective Entity Resolution with Multi-Focal Attention · ACL (1) 2016 |
Natural language and speech › Information extraction and text analysis › syntactic parsing
dependency parsing |
0.1 | 1 | 2011 | Training dependency parsers by jointly optimizing multiple objectives · EMNLP 2011 |
Machine learning › Kernel, tree and ensemble methods
kernel methods |
0.1 | 1 | 2010 | Training and Testing Low-degree Polynomial Data Mappings via Linear SVM · J. Mach. Learn. Res. 2010 |
Natural language and speech › Information extraction and text analysis › semantic parsing
question parsing |
0.1 | 1 | 2010 | Uptraining for Accurate Deterministic Question Parsing · EMNLP 2010 |
Machine learning › Kernel, tree and ensemble methods
support vector machine |
0.1 | 1 | 2010 | Training and Testing Low-degree Polynomial Data Mappings via Linear SVM · J. Mach. Learn. Res. 2010 |
Machine learning › Optimization for machine learning
multi-objective optimization |
0.0 | 1 | 2011 | Training dependency parsers by jointly optimizing multiple objectives · EMNLP 2011 |
Natural language and speech › Language models and text generation
large language model fine-tuning |
0.0 | 1 | 2010 | Uptraining for Accurate Deterministic Question Parsing · EMNLP 2010 |
Methods — techniques the papers use, named apart from their topics
multi-focal attention · 0.2joint optimization · 0.1uptraining · 0.1polynomial data mapping · 0.1linear SVM · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Collective Entity Resolution with Multi-Focal AttentionabstractAmir Globerson, Nevena Lazic, Soumen Chakrabarti, Amarnag Subramanya, Michael Ringgaard, Fernando Pereira. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016. Amir Globerson, Nevena Lazic, Soumen Chakrabarti, Amarnag Subramanya, Michael Ringgaard, Fernando Pereira 0003 |
ACL (1) | 5 |
| 2015 | Plato: A Selective Context Model for Entity ResolutionabstractWe present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features, and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 107 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets. Nevena Lazic, Amarnag Subramanya, Michael Ringgaard, Fernando Pereira 0003 |
Trans. Assoc. Comput. Linguistics | 3 |
| 2011 | Training dependency parsers by jointly optimizing multiple objectives
Keith B. Hall, Ryan T. McDonald, Jason Katz-Brown, Michael Ringgaard |
EMNLP | 4 |
| 2010 | Uptraining for Accurate Deterministic Question Parsing
Slav Petrov, Pi-Chuan Chang, Michael Ringgaard, Hiyan Alshawi |
EMNLP | 3 |
| 2010 | Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang 0001, Michael Ringgaard, Chih-Jen Lin |
J. Mach. Learn. Res. | 4 |
| 2009 | Using a Dependency Parser to Improve SMT for Subject-Object-Verb Languages
Jaeho Kang, Michael Ringgaard, Franz Josef Och |
HLT-NAACL | 3 |