M
Massimo Poesio
Researcher at Queen Mary University of London
Publications - 286
Citations - 9437
Massimo Poesio is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Coreference & Anaphora (linguistics). The author has an hindex of 44, co-authored 271 publications receiving 8326 citations. Previous affiliations of Massimo Poesio include University of Southern California & University of Essex.
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Journal ArticleDOI
Inter-coder agreement for computational linguistics
TL;DR: It is argued that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasks—but that their use makes the interpretation of the value of the coefficient even harder.
Journal Article
A corpus-based investigation of definite description use
Massimo Poesio,Renata Vieira +1 more
TL;DR: Questions are raised concerning the starategy of evaluating systems for definite description interpretation by comparing their results with a standardized annotation, and the great number of discourse-new definites and the presence of definites that did not seem to require a complete disambiguation.
The TRAINS project: A case study in building a conversational planning agent
James F. Allen,Lenhart K. Schubert,George Ferguson,Peter A. Heeman,Chung Hee Hwang,Tsuneaki Kato,Marc Light,Nathaniel G. Martin,Bradford W. Miller,Massimo Poesio +9 more
TL;DR: The TRAINS project as mentioned in this paper is an effort to build a conversationally proficient planning assistant, which provides a platform for a wide range of issues in natural language understanding, mixed-initiative planning systems, and representing and reasoning about time, actions and events.
Journal ArticleDOI
The TRAINS Project: A Case Study in Defining a Conversational Planning Agent
James F. Allen,Lenhart K. Schubert,George Ferguson,Peter A. Heeman,Chung H Hwang,Tsuneaki Kato,Marc Light,Nathaniel G. Martin,Bradford W. Miller,Massimo Poesio,David Traum +10 more
TL;DR: The TRAINS project is an effort to build a conversationally proficient planning assistant that provides the research platform for a wide range of issues in natural language understanding, mixed-initiative planning systems, and representing and reasoning about time, actions and events.
Proceedings ArticleDOI
Named Entity Recognition as Dependency Parsing.
TL;DR: Ideas from graph-based dependency parsing are used to provide the model a global view on the input via a biaffine model and show that the model works well for both nested and flat NER, through evaluation on 8 corpora and achieving SoTA performance on all of them.