World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
13166
World Ranking
7779
National Ranking
3362

Overview

Ted Pedersen is a researcher affiliated with the University of Minnesota in the United States. Their primary field of study is Computer Science, with a focus on Artificial Intelligence. The scientist's work spans multiple interdisciplinary areas, including Molecular Biology, Social Psychology, and Experimental and Cognitive Psychology.

Their research covers a range of topics in natural language processing and computational linguistics. These topics include:

  • Hate Speech and Cyberbullying Detection
  • Topic Modeling
  • Natural Language Processing Techniques
  • Sentiment Analysis and Opinion Mining
  • Text Readability and Simplification
  • Biomedical Text Mining and Ontologies
  • Humor Studies and Applications

Ted Pedersen's publication record includes several papers in various academic venues. The majority of their work has appeared in arXiv (Cornell University), with a smaller number of publications in other venues such as the Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022).

Notable recent papers authored by Ted Pedersen include:

  • Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets, 2020, arXiv (Cornell University)
  • Duluth at SemEval-2020 Task 7: Using Surprise as a Key to Unlock Humorous Headlines, 2020, arXiv (Cornell University)

The scientist has collaborated with several coauthors including Jennifer D'Souza, Sören Auer, Rupak Kumar Das, Shuning Jin, and Yue Yin. These collaborative efforts have contributed to a variety of topics and publications in the field.

Best Publications

  • WordNet::Similarity: measuring the relatedness of concepts

    Ted Pedersen;Siddharth Patwardhan;Jason Michelizzi

  • An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet

    Satanjeev Banerjee;Ted Pedersen

  • Extended gloss overlaps as a measure of semantic relatedness

    Satanjeev Banerjee;Ted Pedersen

  • Measures of semantic similarity and relatedness in the biomedical domain

    Ted Pedersen;Serguei V. S. Pakhomov;Siddharth Patwardhan;Christopher G. Chute

  • Using measures of semantic relatedness for word sense disambiguation

    Siddharth Patwardhan;Satanjeev Banerjee;Ted Pedersen

  • Using WordNet Based Context Vectors to Estimate the Semantic Relatedness of Concepts

    Siddharth Patwardhan;Ted Pedersen

  • The design, implementation, and use of the Ngram statistics package

    Satanjeev Banerjee;Ted Pedersen

  • An evaluation exercise for word alignment

    Rada Mihalcea;Ted Pedersen

  • Maximizing Semantic Relatedness to Perform Word Sense Disambiguation

    Ted Pedersen;Satanjeev Banerjee;Siddharth Patwardhan

  • Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces

    Amruta Purandare;Ted Pedersen

  • A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation

    Ted Pedersen

  • Semantic Similarity and Relatedness between Clinical Terms: An Experimental Study

    Serguei Pakhomov;Bridget McInnes;Terrence Adam;Ying Liu

  • Distinguishing Word Senses in Untagged Text

    Ted Pedersen;Rebecca F. Bruce

  • SenseRelate targetword: a generalized framework for word sense disambiguation

    Siddharth Patwardhan;Satanjeev Banerjee;Ted Pedersen

  • A decision tree of bigrams is an accurate predictor of word sense

    Ted Pedersen

  • UMLS-Interface and UMLS-Similarity : open source software for measuring paths and semantic similarity.

    Bridget T. McInnes;Ted Pedersen;Serguei V.S. Pakhomov

  • Name discrimination by clustering similar contexts

    Ted Pedersen;Amruta Purandare;Anagha Kulkarni

  • Fishing for Exactness

    Ted Pedersen

  • Abbreviation and acronym disambiguation in clinical discourse.

    Serguei Pakhomov;Ted Pedersen;Christopher G. Chute

  • Offspring from Reproduction Problems: What Replication Failure Teaches Us

    Antske Fokkens;Marieke van Erp;Marten Postma;Ted Pedersen

  • Gloss overlaps as a measure of semantic relatedness

    Satanjeev Banerjee;Ted Pedersen

Frequent Co-Authors

Serguei V. S. Pakhomov
Serguei V. S. Pakhomov University of Minnesota
Thamar Solorio
Thamar Solorio Mohamed bin Zayed University of Artificial Intelligence
Christopher G. Chute
Christopher G. Chute Johns Hopkins University
Janyce Wiebe
Janyce Wiebe University of Pittsburgh
Steven Bethard
Steven Bethard University of Arizona
Rada Mihalcea
Rada Mihalcea University of Michigan–Ann Arbor
Jill Burstein
Jill Burstein Princeton University
Saif M. Mohammad
Saif M. Mohammad National Research Council Canada
Christof Monz
Christof Monz University of Amsterdam
Martha Palmer
Martha Palmer University of Colorado Boulder

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