Demo Data from EDBT 2026 / reviewers in the wild / expert
Samuel Brody
dblp:46/4636
· DBLP profile ↗
9ranked-venue papers
7as 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 · 7 · 7 first-authorApplied, interdisciplinary, general and emerging computing · 2Databases, data management, data science and information retrieval · 1Human-computer interaction and ubiquitous 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
4 papers |
Information extraction and text analysis · 71% Machine translation · 16% Question answering and dialogue systems · 10% |
Topics — the 8 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
sentiment analysis |
0.1 | 1 | 2011 | Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! Using Word Lengthening to Detect Sentiment in Microblogs · EMNLP 2011 |
Natural language and speech › Information extraction and text analysis › syntactic parsing
dependency parsing |
0.1 | 1 | 2010 | It Depends on the Translation: Unsupervised Dependency Parsing via Word Alignment · EMNLP 2010 |
Natural language and speech › Information extraction and text analysis
syntactic parsing |
0.1 | 1 | 2010 | It Depends on the Translation: Unsupervised Dependency Parsing via Word Alignment · EMNLP 2010 |
Natural language and speech › Machine translation › statistical machine translation
word alignment |
0.1 | 1 | 2010 | It Depends on the Translation: Unsupervised Dependency Parsing via Word Alignment · EMNLP 2010 |
Natural language and speech › Question answering and dialogue systems › knowledge base question answering
relation detection |
0.1 | 1 | 2007 | Clustering Clauses for High-Level Relation Detection: An Information-theoretic Approach · ACL 2007 |
Natural language and speech › Information extraction and text analysis › word sense disambiguation
unsupervised word sense disambiguation |
0.1 | 1 | 2006 | Ensemble Methods for Unsupervised WSD · ACL 2006 |
Natural language and speech › Information extraction and text analysis
word sense disambiguation |
0.1 | 1 | 2006 | Ensemble Methods for Unsupervised WSD · ACL 2006 |
Machine learning › Kernel, tree and ensemble methods
ensemble learning |
0.0 | 1 | 2006 | Ensemble Methods for Unsupervised WSD · ACL 2006 |
Methods — techniques the papers use, named apart from their topics
word lengthening detection · 0.1unsupervised learning · 0.1information-theoretic clustering · 0.1voting-based combination · 0.1arbiter-based combination · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Characterizing the Sublanguage of Online Breast Cancer Forums for Medications, Symptoms, and Emotions
Noémie Elhadad, Shaodian Zhang, Patricia Driscoll, Samuel Brody |
AMIA | 4 |
| 2012 | On the Study of Diurnal Urban Routines on Twitter
Mor Naaman, Amy X. Zhang, Samuel Brody, Gilad Lotan |
ICWSM | 3 |
| 2011 | Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! Using Word Lengthening to Detect Sentiment in Microblogs
Samuel Brody, Nicholas Diakopoulos |
EMNLP | 1 |
| 2010 | It Depends on the Translation: Unsupervised Dependency Parsing via Word Alignment
Samuel Brody |
EMNLP | 1 |
| 2010 | An Unsupervised Aspect-Sentiment Model for Online Reviews
Samuel Brody, Noémie Elhadad |
HLT-NAACL | 1 |
| 2009 | Bayesian Word Sense Induction
Samuel Brody, Mirella Lapata |
EACL | 1 |
| 2008 | Good Neighbors Make Good Senses: Exploiting Distributional Similarity for Unsupervised WSD
Samuel Brody, Mirella Lapata |
COLING | 1 |
| 2007 | Clustering Clauses for High-Level Relation Detection: An Information-theoretic Approach
Samuel Brody |
ACL | 1 |
| 2006 | Ensemble Methods for Unsupervised WSDabstractCombination methods are an effective way of improving system performance. This paper examines the benefits of system combination for unsupervised WSD. We investigate several voting- and arbiter-based combination strategies over a diverse pool of unsupervised WSD systems. Our combination methods rely on predominant senses which are derived automatically from raw text. Experiments using the SemCor and Senseval-3 data sets demonstrate that our ensembles yield significantly better results when compared with state-of-the-art. Samuel Brody, Roberto Navigli, Mirella Lapata |
ACL | 1 |