Demo Data from VLDB 2026 / reviewers in the wild / expert
Stijn De Saeger
dblp:76/4297
· DBLP profile ↗
20ranked-venue papers
3as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 20 · 3 first-authorDatabases, data management, data science and information retrieval · 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.
| Artificial intelligence
11 papers |
Information extraction and text analysis · 69% Knowledge representation and reasoning · 16% Question answering and dialogue systems · 10% | |
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 100% |
Topics — the 13 heaviest of 17, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
relation extraction |
0.2 | 2 | 2009 | Large Scale Relation Acquisition Using Class Dependent Patterns · ICDM 2009 Large-Scale Verb Entailment Acquisition from the Web · EMNLP 2009 |
Natural language and speech › Question answering and dialogue systems › reasoning-based question answering
why-question answering |
0.2 | 1 | 2013 | Why-Question Answering using Intra- and Inter-Sentential Causal Relations · ACL (1) 2013 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
commonsense reasoning |
0.1 | 1 | 2012 | Excitatory or Inhibitory: A New Semantic Orientation Extracts Contradiction and Causality from the Web · EMNLP-CoNLL 2012 |
Natural language and speech › Information extraction and text analysis
paraphrase extraction |
0.1 | 1 | 2011 | Extracting Paraphrases from Definition Sentences on the Web · ACL 2011 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
bayesian inference |
0.1 | 1 | 2010 | A Bayesian Method for Robust Estimation of Distributional Similarities · ACL 2010 |
Natural language and speech › Information extraction and text analysis › distributional semantics
distributional similarity |
0.1 | 1 | 2010 | A Bayesian Method for Robust Estimation of Distributional Similarities · ACL 2010 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › semantic representation › semantic relations
hypernym discovery |
0.1 | 1 | 2009 | Hypernym Discovery Based on Distributional Similarity and Hierarchical Structures · EMNLP 2009 |
Natural language and speech › Information extraction and text analysis › relation extraction › event relation extraction
causal relation extraction |
0.0 | 1 | 2013 | Why-Question Answering using Intra- and Inter-Sentential Causal Relations · ACL (1) 2013 |
Collaborative and social computing
social media |
0.0 | 1 | 2013 | Aid is Out There: Looking for Help from Tweets during a Large Scale Disaster · ACL (1) 2013 |
Natural language and speech › Information extraction and text analysis
sentiment analysis |
0.0 | 1 | 2012 | Why Question Answering using Sentiment Analysis and Word Classes · EMNLP-CoNLL 2012 |
Natural language and speech › Information extraction and text analysis › text mining
web text mining |
0.0 | 1 | 2011 | Extracting Paraphrases from Definition Sentences on the Web · ACL 2011 |
Natural language and speech › Information extraction and text analysis
pattern learning |
0.0 | 1 | 2009 | Large Scale Relation Acquisition Using Class Dependent Patterns · ICDM 2009 |
Web and social media mining › web mining
web text mining |
0.0 | 1 | 2009 | Large-Scale Verb Entailment Acquisition from the Web · EMNLP 2009 |
Methods — techniques the papers use, named apart from their topics
tweet analysis · 0.3distributional similarity · 0.3web-scale pattern extraction · 0.2supervised classification · 0.2word classes · 0.1web-based pattern mining · 0.1semantic orientation · 0.1partial pattern matching · 0.1paraphrase extraction · 0.1bayesian inference · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Why-Question Answering using Intra- and Inter-Sentential Causal Relations
Jong-Hoon Oh, Kentaro Torisawa, Chikara Hashimoto, Motoki Sano, Stijn De Saeger, Kiyonori Ohtake |
ACL (1) | 5 |
| 2013 | Aid is Out There: Looking for Help from Tweets during a Large Scale Disaster
István Varga, Motoki Sano, Kentaro Torisawa, Chikara Hashimoto, Kiyonori Ohtake, Takao Kawai, Jong-Hoon Oh, Stijn De Saeger |
ACL (1) | 8 |
| 2013 | Two-Stage Method for Large-Scale Acquisition of Contradiction Pattern Pairs using EntailmentabstractJulien Kloetzer, Stijn De Saeger, Kentaro Torisawa, Chikara Hashimoto, Jong-Hoon Oh, Motoki Sano, Kiyonori Ohtake. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013. Julien Kloetzer, Stijn De Saeger, Kentaro Torisawa, Chikara Hashimoto, Jong-Hoon Oh, Motoki Sano, Kiyonori Ohtake |
EMNLP | 2 |
| 2013 | NICT Disaster Information Analysis System
Kiyonori Ohtake, Jun Goto, Stijn De Saeger, Kentaro Torisawa, Junta Mizuno, Kentaro Inui |
IJCNLP | 3 |
| 2013 | WISDOM2013: A Large-scale Web Information Analysis System
Masahiro Tanaka, Stijn De Saeger, Kiyonori Ohtake, Chikara Hashimoto, Makoto Hijiya, Hideaki Fujii, Kentaro Torisawa |
IJCNLP | 2 |
| 2013 | Minimally Supervised Method for Multilingual Paraphrase Extraction from Definition Sentences on the Web
Yulan Yan, Chikara Hashimoto, Kentaro Torisawa, Takao Kawai, Jun'ichi Kazama, Stijn De Saeger |
HLT-NAACL | 6 |
| 2012 | Excitatory or Inhibitory: A New Semantic Orientation Extracts Contradiction and Causality from the Web
Chikara Hashimoto, Kentaro Torisawa, Stijn De Saeger, Jong-Hoon Oh, Jun'ichi Kazama |
EMNLP-CoNLL | 3 |
| 2012 | Why Question Answering using Sentiment Analysis and Word Classes
Jong-Hoon Oh, Kentaro Torisawa, Chikara Hashimoto, Takuya Kawada, Stijn De Saeger, Jun'ichi Kazama, Yiou Wang |
EMNLP-CoNLL | 5 |
| 2011 | Extracting Paraphrases from Definition Sentences on the Web
Chikara Hashimoto, Kentaro Torisawa, Stijn De Saeger, Jun'ichi Kazama, Sadao Kurohashi |
ACL | 3 |
| 2011 | Relation Acquisition using Word Classes and Partial Patterns
Stijn De Saeger, Kentaro Torisawa, Masaaki Tsuchida, Jun'ichi Kazama, Chikara Hashimoto, Ichiro Yamada, Jong-Hoon Oh, István Varga, Yulan Yan |
EMNLP | 1 |
| 2011 | Toward Finding Semantic Relations not Written in a Single Sentence: An Inference Method using Auto-Discovered Rules
Masaaki Tsuchida, Kentaro Torisawa, Stijn De Saeger, Jong-Hoon Oh, Jun'ichi Kazama, Chikara Hashimoto, Hayato Ohwada |
IJCNLP | 3 |
| 2011 | Similarity Based Language Model Construction for Voice Activated Open-Domain Question Answering
István Varga, Kiyonori Ohtake, Kentaro Torisawa, Stijn De Saeger, Teruhisa Misu, Shigeki Matsuda, Jun'ichi Kazama |
IJCNLP | 4 |
| 2011 | Extending WordNet with Hypernyms and Siblings Acquired from Wikipedia
Ichiro Yamada, Jong-Hoon Oh, Chikara Hashimoto, Kentaro Torisawa, Jun'ichi Kazama, Stijn De Saeger, Takuya Kawada |
IJCNLP | 6 |
| 2010 | A Bayesian Method for Robust Estimation of Distributional Similarities
Jun'ichi Kazama, Stijn De Saeger, Kow Kuroda, Masaki Murata, Kentaro Torisawa |
ACL | 2 |
| 2010 | Co-STAR: A Co-training Style Algorithm for Hyponymy Relation Acquisition from Structured and Unstructured Text
Jong-Hoon Oh, Ichiro Yamada, Kentaro Torisawa, Stijn De Saeger |
COLING | 4 |
| 2010 | Generation of Summaries that Appropriately and Adequately Express the Contents of Original Documents Using Word-Association Knowledge
Kazuki Takigawa, Masaki Murata, Masaaki Tsuchida, Stijn De Saeger, Kazuhide Yamamoto, Kentaro Torisawa |
PACLIC | 4 |
| 2009 | Large-Scale Verb Entailment Acquisition from the Web
Chikara Hashimoto, Kentaro Torisawa, Kow Kuroda, Stijn De Saeger, Masaki Murata, Jun'ichi Kazama |
EMNLP | 4 |
| 2009 | Hypernym Discovery Based on Distributional Similarity and Hierarchical Structures
Ichiro Yamada, Kentaro Torisawa, Jun'ichi Kazama, Kow Kuroda, Masaki Murata, Stijn De Saeger, Francis Bond, Asuka Sumida |
EMNLP | 6 |
| 2009 | Large Scale Relation Acquisition Using Class Dependent PatternsabstractThis paper proposes a minimally supervised method for acquiring high-level semantic relations such as causality and prevention from the Web. Our method learns linguistic patterns that express causality such as “x gave rise to y”, and uses them to extract causal noun pairs like (global warming, malaria epidemic) from sentences like “global warming gave rise to a new malaria epidemic”. The novelty of our method lies in the use of semantic word classes acquired by large scale clustering for learning class dependent patterns. We demonstrate the effectiveness of this class based approach on three large-scale relation mining tasks from 50 million Japanese Web pages. In two of these tasks we obtained more than 30,000 relation instances with over 80% precision, outperforming a state-of-the-art system by a large margin. Stijn De Saeger, Kentaro Torisawa, Jun'ichi Kazama, Kow Kuroda, Masaki Murata |
ICDM | 1 |
| 2008 | Looking for Trouble
Stijn De Saeger, Kentaro Torisawa, Jun'ichi Kazama |
COLING | 1 |