Demo Data from SIGIR 2026 / reviewers in the wild / expert
Bill MacCartney
dblp:36/2275
· DBLP profile ↗
6ranked-venue papers
4as 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 · 6 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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
2 papers |
Language models and text generation · 32% Machine translation · 32% Information extraction and text analysis · 32% | |
| Theoretical computer science
1 paper |
Automated reasoning and model checking · 100% |
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 › Language models and text generation › natural language understanding › sentence pair modeling
natural language inference |
0.1 | 1 | 2008 | A Phrase-Based Alignment Model for Natural Language Inference · EMNLP 2008 |
Natural language and speech › Information extraction and text analysis › multilingual NLP
phrase alignment |
0.1 | 1 | 2008 | A Phrase-Based Alignment Model for Natural Language Inference · EMNLP 2008 |
Natural language and speech › Machine translation › statistical machine translation
word alignment |
0.1 | 1 | 2008 | A Phrase-Based Alignment Model for Natural Language Inference · EMNLP 2008 |
Automated reasoning and model checking
theorem proving |
0.0 | 1 | 2003 | Practical Partition-Based Theorem Proving for Large Knowledge Bases · IJCAI 2003 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge-based systems
large knowledge bases |
0.0 | 1 | 2003 | Practical Partition-Based Theorem Proving for Large Knowledge Bases · IJCAI 2003 |
Methods — techniques the papers use, named apart from their topics
phrase-based alignment model · 0.1partitioning · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | On the Importance of Text Analysis for Stock Price Prediction
Heeyoung Lee 0004, Mihai Surdeanu, Bill MacCartney, Daniel Jurafsky |
LREC | 3 |
| 2008 | Modeling Semantic Containment and Exclusion in Natural Language Inference
Bill MacCartney, Christopher D. Manning |
COLING | 1 |
| 2008 | A Phrase-Based Alignment Model for Natural Language Inference
Bill MacCartney, Michel Galley, Christopher D. Manning |
EMNLP | 1 |
| 2006 | Generating Typed Dependency Parses from Phrase Structure Parses
Marie-Catherine de Marneffe, Bill MacCartney, Christopher D. Manning |
LREC | 2 |
| 2006 | Learning to recognize features of valid textual entailments
Bill MacCartney, Trond Grenager, Marie-Catherine de Marneffe, Daniel M. Cer, Christopher D. Manning |
HLT-NAACL | 1 |
| 2003 | Practical Partition-Based Theorem Proving for Large Knowledge Bases
Bill MacCartney, Sheila A. McIlraith, Eyal Amir, Tomás E. Uribe |
IJCAI | 1 |