Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Inter-Coder Agreement for Computational Linguistics
20081.0k citationsMassimo Poesio et al.Computational Linguisticsprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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Countries citing papers authored by Massimo Poesio
Since
Specialization
Citations
This map shows the geographic impact of Massimo Poesio's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Massimo Poesio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Massimo Poesio more than expected).
This network shows the impact of papers produced by Massimo Poesio. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Massimo Poesio. The network helps show where Massimo Poesio may publish in the future.
Co-authorship network of co-authors of Massimo Poesio
This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Poesio.
A scholar is included among the top collaborators of Massimo Poesio based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Massimo Poesio. Massimo Poesio is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chamberlain, Jon, et al.. (2019). Metrics of games-with-a-purpose for NLP applications. Queen Mary Research Online (Queen Mary University of London).4 indexed citations
12.
Celli, Fabio, Evgeny A. Stepanov, Massimo Poesio, & Giuseppe Riccardi. (2016). Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters. International Conference on Computational Linguistics. 110–118.13 indexed citations
13.
Uryupina, Olga, Alessandro Moschitti, & Massimo Poesio. (2012). BART goes multilingual: The UniTN / Essex submission to the CoNLL-2012 Shared Task. Empirical Methods in Natural Language Processing. 122–128.12 indexed citations
14.
Uryupina, Olga & Massimo Poesio. (2012). Domain-specific vs. Uniform Modeling for Coreference Resolution. Language Resources and Evaluation. 187–191.7 indexed citations
15.
Fornaciari, Tommaso & Massimo Poesio. (2011). Lexical vs. Surface Features in Deceptive Language Analysis. Institutional Research Information System (Università degli Studi di Trento).8 indexed citations
16.
Saha, Sriparna, Asif Ekbal, Olga Uryupina, & Massimo Poesio. (2011). Single and multi-objective optimization for feature selection in anaphora resolution. International Joint Conference on Natural Language Processing. 93–101.8 indexed citations
17.
Recasens, Marta Vilar, Lluı́s Màrquez, M. Antònia Martí, et al.. (2010). SemEval-2010 Task 1: Coreference Resolution in Multiple Languages. Ghent University Academic Bibliography (Ghent University). 1–8.104 indexed citations
Almuhareb, Abdulrahman & Massimo Poesio. (2006). MSDA: Wordsense Discrimination Using Context Vectors and Attributes. Institutional Research Information System (Università degli Studi di Trento). 543–547.3 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.