Emmanuele Chersoni

795 total citations
54 papers, 326 citations indexed

About

Emmanuele Chersoni is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Molecular Biology. According to data from OpenAlex, Emmanuele Chersoni has authored 54 papers receiving a total of 326 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 12 papers in Cognitive Neuroscience and 9 papers in Molecular Biology. Recurrent topics in Emmanuele Chersoni's work include Topic Modeling (39 papers), Natural Language Processing Techniques (33 papers) and Text Readability and Simplification (12 papers). Emmanuele Chersoni is often cited by papers focused on Topic Modeling (39 papers), Natural Language Processing Techniques (33 papers) and Text Readability and Simplification (12 papers). Emmanuele Chersoni collaborates with scholars based in Hong Kong, United States and Italy. Emmanuele Chersoni's co-authors include Enrico Santus, Chu‐Ren Huang, Alessandro Lenci, Rong Xiang, Philippe Blache, Giuseppe Serra, Qin Lu, Nora Hollenstein, Cassandra L. Jacobs and Yohei Oseki and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Frontiers in Psychology.

In The Last Decade

Emmanuele Chersoni

45 papers receiving 312 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Emmanuele Chersoni Hong Kong 12 226 57 34 31 17 54 326
Thomas Icard United States 11 261 1.2× 113 2.0× 40 1.2× 9 0.3× 6 0.4× 47 468
Archna Bhatia United States 9 310 1.4× 27 0.5× 41 1.2× 15 0.5× 33 1.9× 26 412
Pierrette Bouillon Switzerland 13 503 2.2× 17 0.3× 59 1.7× 68 2.2× 20 1.2× 96 615
Marjorie McShane United States 13 365 1.6× 17 0.3× 18 0.5× 42 1.4× 35 2.1× 59 457
Simone Conia Italy 10 270 1.2× 12 0.2× 12 0.4× 13 0.4× 14 0.8× 25 352
John K. Pate Australia 7 234 1.0× 24 0.4× 62 1.8× 12 0.4× 24 1.4× 16 338
Emiel van Miltenburg Netherlands 11 386 1.7× 26 0.5× 25 0.7× 18 0.6× 40 2.4× 29 494
Jeroen Geertzen Netherlands 9 232 1.0× 78 1.4× 50 1.5× 11 0.4× 9 0.5× 23 425
Matej Martinc Slovenia 9 230 1.0× 15 0.3× 23 0.7× 16 0.5× 37 2.2× 32 289
Magdalena Wolska Germany 11 242 1.1× 19 0.3× 50 1.5× 30 1.0× 48 2.8× 48 472

Countries citing papers authored by Emmanuele Chersoni

Since Specialization
Citations

This map shows the geographic impact of Emmanuele Chersoni'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 Emmanuele Chersoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emmanuele Chersoni more than expected).

Fields of papers citing papers by Emmanuele Chersoni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Emmanuele Chersoni. 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 Emmanuele Chersoni. The network helps show where Emmanuele Chersoni may publish in the future.

Co-authorship network of co-authors of Emmanuele Chersoni

This figure shows the co-authorship network connecting the top 25 collaborators of Emmanuele Chersoni. A scholar is included among the top collaborators of Emmanuele Chersoni 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 Emmanuele Chersoni. Emmanuele Chersoni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bondielli, Alessandro, et al.. (2025). ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models. CINECA IRIS Institutial research information system (University of Pisa). 17335–17355.
2.
Chersoni, Emmanuele, et al.. (2024). Neural Generative Models and the Parallel Architecture of Language: A Critical Review and Outlook. Topics in Cognitive Science. 17(4). 948–961.
3.
Chersoni, Emmanuele, et al.. (2024). Can Large Language Models Interpret Noun-Noun Compounds? A Linguistically-Motivated Study on Lexicalized and Novel Compounds. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 11823–11835.
4.
Chersoni, Emmanuele, et al.. (2024). CompLex-ZH: A New Dataset for Lexical Complexity Prediction in Mandarin and Cantonese. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 20–26.
5.
Cappilli, Simone, Emmanuele Chersoni, Antonella Santuccione Chadha, et al.. (2023). Towards AI-driven longevity research: An overview. SHILAP Revista de lepidopterología. 4. 1057204–1057204. 17 indexed citations
6.
Li, Junlin, et al.. (2023). Comparing and Predicting Eye-tracking Data of Mandarin and Cantonese. 121–132. 2 indexed citations
7.
Chersoni, Emmanuele, et al.. (2023). Extensive evaluation of transformer-based architectures for adverse drug events extraction. Knowledge-Based Systems. 275. 110675–110675. 7 indexed citations
8.
Kauf, Carina, et al.. (2023). Event Knowledge in Large Language Models: The Gap Between the Impossible and the Unlikely. Cognitive Science. 47(11). e13386–e13386. 26 indexed citations
9.
Cong, Yan, et al.. (2023). Are Language Models Sensitive to Semantic Attraction? A Study on Surprisal. CINECA IRIS Institutial research information system (University of Pisa). 141–148. 6 indexed citations
10.
Chersoni, Emmanuele, et al.. (2023). We Understand Elliptical Sentences, and Language Models should Too: A New Dataset for Studying Ellipsis and its Interaction with Thematic Fit. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 1 indexed citations
11.
Chersoni, Emmanuele, et al.. (2023). Investigating the Effect of Discourse Connectives on Transformer Surprisal: Language Models Understand Connectives, Even So They Are Surprised. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 222–232. 1 indexed citations
12.
Chersoni, Emmanuele, et al.. (2022). Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation. Experimental Biology and Medicine. 247(22). 2003–2014. 5 indexed citations
13.
Chersoni, Emmanuele, et al.. (2022). Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets. Journal of Medical Internet Research. 24(5). e35115–e35115. 13 indexed citations
14.
Liu, Chenxin & Emmanuele Chersoni. (2022). Exploring Nominal Coercion in Semantic Spaces with Static and Contextualized Word Embeddings. 49–57. 1 indexed citations
15.
16.
Santus, Enrico, et al.. (2021). Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 3534–3546. 8 indexed citations
17.
Xiang, Rong, Emmanuele Chersoni, Qin Lu, et al.. (2021). Lexical data augmentation for sentiment analysis. Journal of the Association for Information Science and Technology. 72(11). 1432–1447. 26 indexed citations
18.
Chersoni, Emmanuele, et al.. (2020). Are word embeddings really a bad fit for the estimation of thematic fit. Language Resources and Evaluation. 5708–5713. 2 indexed citations
19.
Chersoni, Emmanuele, Rong Xiang, Qin Lu, & Chu‐Ren Huang. (2020). Automatic learning of modality exclusivity norms with crosslingual word embeddings. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 32–38. 4 indexed citations
20.
Xiang, Rong, Yunfei Long, Anran Li, et al.. (2020). Ciron : a new benchmark dataset for Chinese irony detection. Language Resources and Evaluation. 5714–5720. 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.

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