Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ted Pedersen
dblp:71/790
· DBLP profile ↗
47ranked-venue papers
15as first author
0since 2021 · last 2016
0000-0003-0417-4123ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 36 · 14 first-authorApplied, interdisciplinary, general and emerging computing · 11 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 9 · 2 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 · 85% Knowledge representation and reasoning · 12% Trustworthy machine learning · 2% | |
| Databases, data mining, and information retrieval
2 papers |
Information retrieval · 40% Data integration and cleaning · 30% Knowledge graphs · 30% | |
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% |
Topics — the 16 heaviest of 18, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
word sense disambiguation |
0.3 | 5 | 2006 | An End-to-End Supervised Target-Word Sense Disambiguation System · AAAI 2006 Kernel Methods for Word Sense Disambiguation and Acronym Expansion · AAAI 2006 SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation · ACL 2005 |
Empirical software engineering
reproducibility |
0.2 | 1 | 2013 | Offspring from Reproduction Problems: What Replication Failure Teaches Us · ACL (1) 2013 |
Natural language and speech › Information extraction and text analysis › word sense disambiguation
word sense discrimination |
0.1 | 2 | 2005 | Identifying Similar Words and Contexts in Natural Language with SenseClusters · AAAI 2005 Discriminating Among Word Meanings by Identifying Similar Contexts · AAAI 2004 |
Natural language and speech › Information extraction and text analysis › text normalization
acronym expansion |
0.1 | 1 | 2006 | Kernel Methods for Word Sense Disambiguation and Acronym Expansion · AAAI 2006 |
Knowledge graphs
entity linking |
0.1 | 1 | 2006 | How Many Different "John Smiths", and Who Are They? · AAAI 2006 |
Data integration and cleaning
entity resolution |
0.1 | 1 | 2006 | How Many Different "John Smiths", and Who Are They? · AAAI 2006 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology |
0.0 | 1 | 2004 | WordNet: : Similarity - Measuring the Relatedness of Concepts · AAAI 2004 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology › lexical ontology
wordnet |
0.0 | 1 | 2004 | WordNet: : Similarity - Measuring the Relatedness of Concepts · AAAI 2004 |
Natural language and speech › Information extraction and text analysis › lexical semantics
word sense induction |
0.0 | 1 | 2004 | SenseClusters - Finding Clusters that Represent Word Senses · AAAI 2004 |
Natural language and speech › Information extraction and text analysis › lexical semantics
semantic relatedness |
0.0 | 1 | 2003 | Extended Gloss Overlaps as a Measure of Semantic Relatedness · IJCAI 2003 |
Information retrieval
retrieval models |
0.0 | 1 | 2003 | Extended Gloss Overlaps as a Measure of Semantic Relatedness · IJCAI 2003 |
Information retrieval › similarity measure
semantic similarity |
0.0 | 1 | 2003 | Extended Gloss Overlaps as a Measure of Semantic Relatedness · IJCAI 2003 |
Natural language and speech › Information extraction and text analysis
distributional semantics |
0.0 | 1 | 2005 | Identifying Similar Words and Contexts in Natural Language with SenseClusters · AAAI 2005 |
Natural language and speech › Information extraction and text analysis
lexical semantics |
0.0 | 1 | 2005 | SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation · AAAI 2005 |
Natural language and speech › Information extraction and text analysis › text similarity
semantic similarity |
0.0 | 1 | 2004 | WordNet: : Similarity - Measuring the Relatedness of Concepts · AAAI 2004 |
Natural language and speech › Language models and text generation
natural language understanding |
0.0 | 1 | 1997 | Distinguishing Word Senses in Untagged Text · EMNLP 1997 |
Methods — techniques the papers use, named apart from their topics
clustering · 0.1gloss overlap · 0.1supervised learning · 0.1kernel methods · 0.1target word disambiguation · 0.1singular value decomposition · 0.1sense inventory · 0.1ontology-based similarity measures · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Adam Kilgarriff's Legacy to Computational Linguistics and Beyond
Roger Evans, Alexander F. Gelbukh, Gregory Grefenstette, Patrick Hanks, Milos Jakubícek, Diana McCarthy, Martha Palmer, Ted Pedersen, Michael Rundell, Pavel Rychlý, Serge Sharoff, David Tugwell |
CICLing (1) | 8 |
| 2016 | Age and Gender Prediction on Health Forum Data
Prasha Shrestha, Nicolas Rey-Villamizar, Farig Sadeque, Ted Pedersen, Steven Bethard, Thamar Solorio |
LREC | 4 |
| 2015 | Evaluating semantic similarity and relatedness over the semantic grouping of clinical term pairs
Bridget T. McInnes, Ted Pedersen |
J. Biomed. Informatics | 2 |
| 2014 | U-path: An undirected path-based measure of semantic similarity
Bridget T. McInnes, Ted Pedersen, Ying Liu 0042, Genevieve B. Melton, Serguei V. S. Pakhomov |
AMIA | 2 |
| 2013 | Offspring from Reproduction Problems: What Replication Failure Teaches Us
Antske Fokkens, Marieke van Erp, Marten Postma, Ted Pedersen, Piek Vossen, Nuno Freire 0001 |
ACL (1) | 4 |
| 2013 | UMLS: : Similarity: Measuring the Relatedness and Similarity of Biomedical Concepts
Bridget T. McInnes, Ted Pedersen, Serguei V. S. Pakhomov, Ying Liu 0042, Genevieve B. Melton |
HLT-NAACL | 2 |
| 2013 | Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text
Bridget T. McInnes, Ted Pedersen |
J. Biomed. Informatics | 2 |
| 2012 | Evaluating Semantic Relatedness and Similarity Measures with Standardized MedDRA Queries
Robert Bill, Ying Liu 0042, Bridget T. McInnes, Genevieve B. Melton, Ted Pedersen, Serguei V. S. Pakhomov |
AMIA | 5 |
| 2012 | Using SemRep to Label Semantic Relations Extracted from Clinical Text
Ying Liu 0042, Robert Bill, Marcelo Fiszman, Thomas C. Rindflesch, Ted Pedersen, Genevieve B. Melton, Serguei V. S. Pakhomov |
AMIA | 5 |
| 2011 | Using Second-order Vectors in a Knowledge-based Method for Acronym Disambiguation
Bridget T. McInnes, Ted Pedersen, Ying Liu 0042, Serguei V. S. Pakhomov, Genevieve B. Melton |
CoNLL | 2 |
| 2011 | Towards a framework for developing semantic relatedness reference standards
Serguei V. S. Pakhomov, Ted Pedersen, Bridget T. McInnes, Genevieve B. Melton, Alexander Ruggieri, Christopher G. Chute |
J. Biomed. Informatics | 2 |
| 2010 | Information Content Measures of Semantic Similarity Perform Better Without Sense-Tagged Text
Ted Pedersen |
HLT-NAACL | 1 |
| 2009 | UMLS-Interface and UMLS-Similarity : Open Source Software for Measuring Paths and Semantic Similarity
Bridget T. McInnes, Ted Pedersen, Serguei V. S. Pakhomov |
AMIA | 2 |
| 2009 | Improved Unsupervised Name Discrimination with Very Wide Bigrams and Automatic Cluster Stopping
Ted Pedersen |
CICLing | 1 |
| 2008 | Empiricism Is Not a Matter of Faithabstract“Hurrah, this is it!” you exclaim as you set down the most recent issue of Computational Linguistics. “This Zigglebottom Tagger is exactly what I need!” A gleeful smile crosses your face as you imagine how your system will improve once you replace your tagger from graduate school with the clearly superior Zigglebottom method. You rub your hands together and page through the article looking for a way to obtain the tagger, but nothing is mentioned. That doesn’t dampen your enthusiasm, so you search the Web, but still nothing turns up. You persist though; those 17 pages of statistically significant results really are impressive. So you e-mail Zigglebottom asking for the tagger. Some days, or perhaps weeks, later, you get a hesitant reply saying: “We’re planning to release a demo version soon, stay tuned . . . ” Or perhaps: “We don’t normally do this, but we can send you a copy (informally) once we clean it up a bit . . . ” Or maybe: “We can’t actually give you the tagger, but you should be able to re-implement it from the article. Just let us know if you have any questions . . . ” Still having faith, and lacking any better alternative, you decide to re-implement the Zigglebottom Tagger. Despite three months of on-and-off effort, the end result provides just the same accuracy as your old tagger, which is nowhere near that reported in the article. Feeling sheepish, you conclude you must have misunderstood something, or maybe there’s a small detail missing from the article. So you contact Zigglebottom again and explain your predicament. He eventually responds: “We’ll look into this right away and get back to you . . . ” A year passes. You have the good fortune to bump into Zigglebottom at the Annual Meeting of the Association for Computational Linguistics (ACL). You angle for a seat next to him during a night out, and you buy him a few beers before you politely resume your quest for the tagger. Finally, he confesses rather glumly: “My student Pifflewhap was the one who did the implementation and ran the experiments, and if he’d only respond to my e-mail I could ask him to tell you how to get it working, but he’s graduated now and is apparently too busy to reply.” After a fewmore beers, Zigglebottom finally agrees to give you the tagger: “I’ll send you the version of the code I have, no promises though!” And true to his word, what he sends is incomplete and undocumented. It doesn’t compile easily, and it’s engineered so that a jumble of programs must be run in an undisclosed kabalistic sequence known only to (perhaps) the elusive Pifflewhap. You try your best to make it work every now Ted Pedersen |
Comput. Linguistics | 1 |
| 2007 | Using UMLS Concept Unique Identifiers (CUIs) for Word Sense Disambiguation in the Biomedical Domain
Bridget T. McInnes, Ted Pedersen, John Carlis |
AMIA | 2 |
| 2007 | Unsupervised Discrimination of Person Names in Web Contexts
Ted Pedersen, Anagha Kulkarni 0001 |
CICLing | 1 |
| 2007 | Measures of semantic similarity and relatedness in the biomedical domain
Ted Pedersen, Serguei V. S. Pakhomov, Siddharth Patwardhan, Christopher G. Chute |
J. Biomed. Informatics | 1 |
| 2006 | Kernel Methods for Word Sense Disambiguation and Acronym Expansion
Mahesh Joshi, Ted Pedersen, Richard Maclin, Serguei V. S. Pakhomov |
AAAI | 2 |
| 2006 | An End-to-End Supervised Target-Word Sense Disambiguation System
Mahesh Joshi, Serguei V. S. Pakhomov, Ted Pedersen, Richard Maclin, Christopher G. Chute |
AAAI | 3 |
| 2006 | How Many Different "John Smiths", and Who Are They?
Anagha Kulkarni 0001, Ted Pedersen |
AAAI | 2 |
| 2006 | A Comparative Study of Supervised Learning as Applied to Acronym Expansion in Clinical Reports
Mahesh Joshi, Serguei V. S. Pakhomov, Ted Pedersen, Christopher G. Chute |
AMIA | 3 |
| 2006 | An Unsupervised Language Independent Method of Name Discrimination Using Second Order Co-occurrence Features
Ted Pedersen, Anagha Kulkarni 0001, Roxana Angheluta, Zornitsa Kozareva, Thamar Solorio |
CICLing | 1 |
| 2006 | Selecting the "Right" Number of Senses Based on Clustering Criterion Functions
Ted Pedersen, Anagha Kulkarni 0001 |
EACL | 1 |
| 2006 | Automatic Cluster Stopping with Criterion Functions and the Gap Statistic
Ted Pedersen, Anagha Kulkarni 0001 |
HLT-NAACL | 1 |
| 2005 | SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation
Siddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen |
AAAI | 3 |
| 2005 | Identifying Similar Words and Contexts in Natural Language with SenseClusters
Ted Pedersen, Anagha Kulkarni 0001 |
AAAI | 1 |
| 2005 | SenseClusters: Unsupervised Clustering and Labeling of Similar Contexts
Anagha Kulkarni 0001, Ted Pedersen |
ACL | 2 |
| 2005 | SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation
Siddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen |
ACL | 3 |
| 2005 | Abbreviation and Acronym Disambiguation in Clinical Discourse
Serguei V. S. Pakhomov, Ted Pedersen, Christopher G. Chute |
AMIA | 2 |
| 2005 | Name Discrimination by Clustering Similar Contexts
Ted Pedersen, Amruta Purandare, Anagha Kulkarni 0001 |
CICLing | 1 |
| 2004 | WordNet: : Similarity - Measuring the Relatedness of Concepts
Ted Pedersen, Siddharth Patwardhan, Jason Michelizzi |
AAAI | 1 |
| 2004 | Discriminating Among Word Meanings by Identifying Similar Contexts
Amruta Purandare, Ted Pedersen |
AAAI | 2 |
| 2004 | SenseClusters - Finding Clusters that Represent Word Senses
Amruta Purandare, Ted Pedersen |
AAAI | 2 |
| 2004 | Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation
Saif M. Mohammad, Ted Pedersen |
CoNLL | 2 |
| 2004 | Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces
Amruta Purandare, Ted Pedersen |
CoNLL | 2 |
| 2003 | The Design, Implementation, and Use of the Ngram Statistics Package
Satanjeev Banerjee, Ted Pedersen |
CICLing | 2 |
| 2003 | Guaranteed Pre-tagging for the Brill Tagger
Saif M. Mohammad, Ted Pedersen |
CICLing | 2 |
| 2003 | Using Measures of Semantic Relatedness for Word Sense Disambiguation
Siddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen |
CICLing | 3 |
| 2003 | Extended Gloss Overlaps as a Measure of Semantic Relatedness
Satanjeev Banerjee, Ted Pedersen |
IJCAI | 2 |
| 2002 | An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
Satanjeev Banerjee, Ted Pedersen |
CICLing | 2 |
| 2002 | A Baseline Methodology for Word Sense Disambiguation
Ted Pedersen |
CICLing | 1 |
| 2001 | Lexical Semantic Ambiguity Resolution with Bigram-Based Decision Trees
Ted Pedersen |
CICLing | 1 |
| 2001 | A Decision Tree of Bigrams is an Accurate Predictor of Word Sense
Ted Pedersen |
NAACL | 1 |
| 1997 | A Statistical Decision Making Method: A Case Study on Prepositional Phrase Attachment
Ted Pedersen, Rebecca F. Bruce |
CoNLL | 2 |
| 1997 | Distinguishing Word Senses in Untagged Text
Ted Pedersen, Rebecca F. Bruce |
EMNLP | 1 |
| 1996 | The Measure of a Model
Rebecca F. Bruce, Janyce Wiebe, Ted Pedersen |
EMNLP | 3 |