World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
12059
World Ranking
4549
National Ranking
276

Overview

Massimo Poesio is affiliated with Queen Mary University of London in the United Kingdom. Their research primarily focuses on computer science, with significant contributions to the subfields of artificial intelligence and cognitive neuroscience. Additional interests include computer vision and pattern recognition, computer science applications, and computer networks and communications.

Their work extensively covers topics related to natural language processing techniques and topic modeling. Other notable research areas include speech and dialogue systems, neurobiology of language and bilingualism, text readability and simplification, mobile crowdsensing and crowdsourcing, as well as memory and neural mechanisms.

Massimo Poesio has collaborated with several frequent coauthors, including Silviu Paun, Juntao Yu, Ron Artstein, Alexandra Uma, and Tommaso Fornaciari.

Their publications appear across various venues, with a significant presence on arXiv (Cornell University). Other venues featuring their research include Frontiers in Artificial Intelligence, Journal of Artificial Intelligence Research, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, and Computational Linguistics.

Notable recent papers include:

  • "Learning from Disagreement: A Survey" (2021), Journal of Artificial Intelligence Research
  • "A Case for Soft Loss Functions" (2020), Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
  • "Named Entity Recognition as Dependency Parsing" (2020), arXiv (Cornell University)
  • "Fake opinion detection: how similar are crowdsourced datasets to real data?" (2020), Language Resources and Evaluation
  • "Scaling and Disagreements: Bias, Noise, and Ambiguity" (2022), Frontiers in Artificial Intelligence

In addition to articles, Massimo Poesio has authored a book titled Statistical Methods for Annotation Analysis published by Morgan & Claypool Publishers in 2022.

Best Publications

  • Inter-coder agreement for computational linguistics

    Ron Artstein;Ron Artstein;Massimo Poesio;Massimo Poesio

  • Named Entity Recognition as Dependency Parsing.

    Juntao Yu;Bernd Bohnet;Massimo Poesio

  • A corpus-based investigation of definite description use

    Massimo Poesio;Renata Vieira

  • BART: A modular toolkit for coreference resolution

    Yannick Versley;Simone Paolo Ponzetto;Massimo Poesio;Vladimir Eidelman

  • The TRAINS Project: A Case Study in Defining a Conversational Planning Agent

    James F. Allen;Lenhart K. Schubert;George Ferguson;Peter Heeman

  • An empirically based system for processing definite descriptions

    Renata Vieira;Massimo Poesio

  • Centering: A Parametric Theory and Its Instantiations

    Massimo Poesio;Rosemary Stevenson;Barbara Di Eugenio;Janet Hitzeman

  • Conversational Actions and Discourse Situations

    Massimo Poesio;David R. Traum

  • Two uses of anaphora resolution in summarization

    Josef Steinberger;Massimo Poesio;Mijail A. Kabadjov;Karel Jeek

  • Strudel: A corpus-based semantic model based on properties and types

    Marco Baroni;Brian Murphy;Eduard Barbu;Massimo Poesio

  • Anaphoric Annotation in the ARRAU Corpus

    Massimo Poesio;Ron Artstein

  • SemEval-2010 Task 1: Coreference Resolution in Multiple Languages

    Marta Recasens;Llu'is Màrquez;Emili Sapena;M. Antònia Mart'i

  • Modelling grounding and discourse obligations using update rules

    Colin Matheson;Massimo Poesio;David Traum

  • The MATE/GNOME Proposals for Anaphoric Annotation, Revisited

    Massimo Poesio

  • Learning to Resolve Bridging References

    Massimo Poesio;Rahul Mehta;Axel Maroudas;Janet Hitzeman

  • Attribute-Based and Value-Based Clustering: An Evaluation.

    Abdulrahman Almuhareb;Massimo Poesio

  • Phrase Detectives: A Web-based collaborative annotation game

    Jon Chamberlain;Massimo Poesio;Udo Kruschwitz

  • Phrase detectives: Utilizing collective intelligence for internet-scale language resource creation

    Massimo Poesio;Jon Chamberlain;Udo Kruschwitz;Livio Robaldo

  • Resolving bridging references in unrestricted text

    Massimo Poesio;Renata Vieira;Simone Teufel

  • Semantic Ambiguity and Perceived Ambiguity

    Massimo Poesio

  • Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

    Nikiforos Karamanis;Massimo Poesio;Chris Mellish;Jon Oberlander

Frequent Co-Authors

Asif Ekbal
Asif Ekbal Indian Institute of Technology Patna
Marco Baroni
Marco Baroni Institució Catalana de Recerca i Estudis Avançats
Simone Paolo Ponzetto
Simone Paolo Ponzetto University of Mannheim
David Traum
David Traum University of Southern California
Chris Mellish
Chris Mellish University of Aberdeen
Alessandro Moschitti
Alessandro Moschitti Amazon (United States)
Jon Oberlander
Jon Oberlander University of Edinburgh
James F. Allen
James F. Allen University of Rochester
Giuseppe Riccardi
Giuseppe Riccardi University of Trento
Michael Strube
Michael Strube Heidelberg Institute for Theoretical Studies

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