What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Natural language processing
Massimo Poesio mostly deals with Artificial intelligence, Natural language processing, Annotation, Semantics and Resolution.
His research integrates issues of Value and Salience in his study of Artificial intelligence.
The various areas that he examines in his Natural language processing study include Interpretation, Mental representation, Utterance, Bridging and Coreference.
His work deals with themes such as Computational linguistics, Gnome, Agreement, Text corpus and Corpus linguistics, which intersect with Annotation.
His Semantics research incorporates elements of Sentence, Similarity and Computational model.
His work in Resolution addresses subjects such as Variety, which are connected to disciplines such as Modular design, Scheme and Information retrieval.
His most cited work include:
- Inter-coder agreement for computational linguistics (916 citations)
- Inter-coder agreement for computational linguistics (916 citations)
- The TRAINS Project: A Case Study in Defining a Conversational Planning Agent (271 citations)
What are the main themes of his work throughout his whole career to date?
Massimo Poesio mainly focuses on Artificial intelligence, Natural language processing, Annotation, Coreference and Resolution.
His Artificial intelligence research is multidisciplinary, relying on both Context and Machine learning.
Massimo Poesio has included themes like Identification, Information retrieval, Ambiguity, Bridging and Arabic in his Natural language processing study.
His Annotation research integrates issues from Interpretation, Agreement, World Wide Web, Scheme and Phrase.
His Phrase research is multidisciplinary, incorporating perspectives in Collective intelligence and Data science.
His Resolution study combines topics from a wide range of disciplines, such as Variety and Anaphora.
He most often published in these fields:
- Artificial intelligence (70.40%)
- Natural language processing (58.84%)
- Annotation (21.30%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (70.40%)
- Natural language processing (58.84%)
- Coreference (17.33%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Coreference, Resolution and Annotation.
Many of his research projects under Artificial intelligence are closely connected to Process with Process, tying the diverse disciplines of science together.
His studies in Natural language processing integrate themes in fields like Anaphora, Arabic and Identification.
His Coreference research is multidisciplinary, incorporating elements of Domain, Bridging, Distinctive feature and Commonsense knowledge.
He has researched Resolution in several fields, including Key, Anaphora, Coherence and Zero.
His Annotation study also includes
- Field which is related to area like Interpretation and SemEval,
- Scheme together with Argument, Feature and Interpretation.
Between 2015 and 2021, his most popular works were:
- Visually grounded and textual semantic models differentially decode brain activity associated with concrete and abstract nouns (42 citations)
- Anaphora Resolution with the ARRAU Corpus (26 citations)
- Comparing Bayesian Models of Annotation (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Natural language processing
Artificial intelligence, Natural language processing, Coreference, Annotation and Anaphora are his primary areas of study.
Massimo Poesio interconnects Crowdsourcing, Gold standard and Identification in the investigation of issues within Artificial intelligence.
Massimo Poesio works on Natural language processing which deals in particular with Dependency grammar.
His study in Coreference is interdisciplinary in nature, drawing from both Interpretation, Probabilistic logic and Natural language.
His Annotation study incorporates themes from Bridging, Field, Bayesian probability, Distinctive feature and Deixis.
His study looks at the relationship between Anaphora and fields such as Variety, as well as how they intersect with chemical problems.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
