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Ted Pedersen

dblp:71/790 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
word sense disambiguation
0.352006
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
0.212013
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
0.122005
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
0.112006
Kernel Methods for Word Sense Disambiguation and Acronym Expansion · AAAI 2006
Knowledge graphs
entity linking
0.112006
How Many Different "John Smiths", and Who Are They? · AAAI 2006
Data integration and cleaning
entity resolution
0.112006
How Many Different "John Smiths", and Who Are They? · AAAI 2006
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology
0.012004
WordNet: : Similarity - Measuring the Relatedness of Concepts · AAAI 2004
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology › lexical ontology
wordnet
0.012004
WordNet: : Similarity - Measuring the Relatedness of Concepts · AAAI 2004
Natural language and speech › Information extraction and text analysis › lexical semantics
word sense induction
0.012004
SenseClusters - Finding Clusters that Represent Word Senses · AAAI 2004
Natural language and speech › Information extraction and text analysis › lexical semantics
semantic relatedness
0.012003
Extended Gloss Overlaps as a Measure of Semantic Relatedness · IJCAI 2003
Information retrieval
retrieval models
0.012003
Extended Gloss Overlaps as a Measure of Semantic Relatedness · IJCAI 2003
Information retrieval › similarity measure
semantic similarity
0.012003
Extended Gloss Overlaps as a Measure of Semantic Relatedness · IJCAI 2003
Natural language and speech › Information extraction and text analysis
distributional semantics
0.012005
Identifying Similar Words and Contexts in Natural Language with SenseClusters · AAAI 2005
Natural language and speech › Information extraction and text analysis
lexical semantics
0.012005
SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation · AAAI 2005
Natural language and speech › Information extraction and text analysis › text similarity
semantic similarity
0.012004
WordNet: : Similarity - Measuring the Relatedness of Concepts · AAAI 2004
Natural language and speech › Language models and text generation
natural language understanding
0.011997
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
YearPublicationVenuePosition
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
LREC4
2015 Evaluating semantic similarity and relatedness over the semantic grouping of clinical term pairs
Bridget T. McInnes, Ted Pedersen
J. Biomed. Informatics2
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
AMIA2
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-NAACL2
2013 Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text
Bridget T. McInnes, Ted Pedersen
J. Biomed. Informatics2
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
AMIA5
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
AMIA5
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
CoNLL2
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. Informatics2
2010 Information Content Measures of Semantic Similarity Perform Better Without Sense-Tagged Text
Ted Pedersen
HLT-NAACL1
2009 UMLS-Interface and UMLS-Similarity : Open Source Software for Measuring Paths and Semantic Similarity
Bridget T. McInnes, Ted Pedersen, Serguei V. S. Pakhomov
AMIA2
2009 Improved Unsupervised Name Discrimination with Very Wide Bigrams and Automatic Cluster Stopping
Ted Pedersen
CICLing1
2008 Empiricism Is Not a Matter of Faith
abstract
“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. Linguistics1
2007 Using UMLS Concept Unique Identifiers (CUIs) for Word Sense Disambiguation in the Biomedical Domain
Bridget T. McInnes, Ted Pedersen, John Carlis
AMIA2
2007 Unsupervised Discrimination of Person Names in Web Contexts
Ted Pedersen, Anagha Kulkarni 0001
CICLing1
2007 Measures of semantic similarity and relatedness in the biomedical domain
Ted Pedersen, Serguei V. S. Pakhomov, Siddharth Patwardhan, Christopher G. Chute
J. Biomed. Informatics1
2006 Kernel Methods for Word Sense Disambiguation and Acronym Expansion
Mahesh Joshi, Ted Pedersen, Richard Maclin, Serguei V. S. Pakhomov
AAAI2
2006 An End-to-End Supervised Target-Word Sense Disambiguation System
Mahesh Joshi, Serguei V. S. Pakhomov, Ted Pedersen, Richard Maclin, Christopher G. Chute
AAAI3
2006 How Many Different "John Smiths", and Who Are They?
Anagha Kulkarni 0001, Ted Pedersen
AAAI2
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
AMIA3
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
CICLing1
2006 Selecting the "Right" Number of Senses Based on Clustering Criterion Functions
Ted Pedersen, Anagha Kulkarni 0001
EACL1
2006 Automatic Cluster Stopping with Criterion Functions and the Gap Statistic
Ted Pedersen, Anagha Kulkarni 0001
HLT-NAACL1
2005 SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation
Siddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen
AAAI3
2005 Identifying Similar Words and Contexts in Natural Language with SenseClusters
Ted Pedersen, Anagha Kulkarni 0001
AAAI1
2005 SenseClusters: Unsupervised Clustering and Labeling of Similar Contexts
Anagha Kulkarni 0001, Ted Pedersen
ACL2
2005 SenseRelate: : TargetWord-A Generalized Framework for Word Sense Disambiguation
Siddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen
ACL3
2005 Abbreviation and Acronym Disambiguation in Clinical Discourse
Serguei V. S. Pakhomov, Ted Pedersen, Christopher G. Chute
AMIA2
2005 Name Discrimination by Clustering Similar Contexts
Ted Pedersen, Amruta Purandare, Anagha Kulkarni 0001
CICLing1
2004 WordNet: : Similarity - Measuring the Relatedness of Concepts
Ted Pedersen, Siddharth Patwardhan, Jason Michelizzi
AAAI1
2004 Discriminating Among Word Meanings by Identifying Similar Contexts
Amruta Purandare, Ted Pedersen
AAAI2
2004 SenseClusters - Finding Clusters that Represent Word Senses
Amruta Purandare, Ted Pedersen
AAAI2
2004 Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation
Saif M. Mohammad, Ted Pedersen
CoNLL2
2004 Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces
Amruta Purandare, Ted Pedersen
CoNLL2
2003 The Design, Implementation, and Use of the Ngram Statistics Package
Satanjeev Banerjee, Ted Pedersen
CICLing2
2003 Guaranteed Pre-tagging for the Brill Tagger
Saif M. Mohammad, Ted Pedersen
CICLing2
2003 Using Measures of Semantic Relatedness for Word Sense Disambiguation
Siddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen
CICLing3
2003 Extended Gloss Overlaps as a Measure of Semantic Relatedness
Satanjeev Banerjee, Ted Pedersen
IJCAI2
2002 An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
Satanjeev Banerjee, Ted Pedersen
CICLing2
2002 A Baseline Methodology for Word Sense Disambiguation
Ted Pedersen
CICLing1
2001 Lexical Semantic Ambiguity Resolution with Bigram-Based Decision Trees
Ted Pedersen
CICLing1
2001 A Decision Tree of Bigrams is an Accurate Predictor of Word Sense
Ted Pedersen
NAACL1
1997 A Statistical Decision Making Method: A Case Study on Prepositional Phrase Attachment
Ted Pedersen, Rebecca F. Bruce
CoNLL2
1997 Distinguishing Word Senses in Untagged Text
Ted Pedersen, Rebecca F. Bruce
EMNLP1
1996 The Measure of a Model
Rebecca F. Bruce, Janyce Wiebe, Ted Pedersen
EMNLP3