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.
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
Countries citing papers authored by Leon Derczynski
Since
Specialization
Citations
This map shows the geographic impact of Leon Derczynski'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 Leon Derczynski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leon Derczynski more than expected).
This network shows the impact of papers produced by Leon Derczynski. 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 Leon Derczynski. The network helps show where Leon Derczynski may publish in the future.
Co-authorship network of co-authors of Leon Derczynski
This figure shows the co-authorship network connecting the top 25 collaborators of Leon Derczynski.
A scholar is included among the top collaborators of Leon Derczynski 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 Leon Derczynski. Leon Derczynski is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Derczynski, Leon, et al.. (2019). Bornholmsk Natural Language Processing: Resources and Tools.. DSpace repository (University of Tartu). 338–344.2 indexed citations
Derczynski, Leon. (2016). Representation and Learning of Temporal Relations. International Conference on Computational Linguistics. 1937–1948.3 indexed citations
8.
Han, Bo, Afshin Rahimi, Leon Derczynski, & Timothy Baldwin. (2016). Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text. International Conference on Computational Linguistics. 213–217.34 indexed citations
9.
Derczynski, Leon, Kalina Bontcheva, & Ian Roberts. (2016). Broad Twitter Corpus: A Diverse Named Entity Recognition Resource. International Conference on Computational Linguistics. 1169–1179.48 indexed citations
10.
Derczynski, Leon. (2016). Complementarity, F-score, and NLP Evaluation. Language Resources and Evaluation. 261–266.46 indexed citations
11.
Derczynski, Leon, et al.. (2015). Tune Your Brown Clustering, Please. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 110–117.10 indexed citations
12.
Roberts, Angus, et al.. (2015). Analysis of Temporal Expressions Annotated in Clinical Notes. UCL Discovery (University College London).5 indexed citations
13.
Derczynski, Leon & Kalina Bontcheva. (2015). Efficient Named Entity Annotation through Pre-empting. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 123–130.1 indexed citations
14.
Derczynski, Leon & Robert Gaizauskas. (2015). Temporal Relation Classification using a Model of Tense and Aspect. Recent Advances in Natural Language Processing. 118–122.2 indexed citations
15.
Sabou, Marta, Kalina Bontcheva, Leon Derczynski, & Arno Scharl. (2014). Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines. Language Resources and Evaluation. 859–866.80 indexed citations
16.
Derczynski, Leon & Kalina Bontcheva. (2014). Pheme: Veracity in Digital Social Networks..25 indexed citations
17.
UzZaman, Naushad, Héctor Llorens, Leon Derczynski, et al.. (2013). SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations. Joint Conference on Lexical and Computational Semantics. 2. 1–9.216 indexed citations
18.
Derczynski, Leon, et al.. (2013). Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data. Recent Advances in Natural Language Processing. 198–206.168 indexed citations
19.
Derczynski, Leon, et al.. (2013). Recognising and Interpreting Named Temporal Expressions. Recent Advances in Natural Language Processing. 113–121.
20.
Llorens, Héctor, Leon Derczynski, Robert Gaizauskas, & Estela Saquete. (2012). TIMEN: An Open Temporal Expression Normalisation Resource. Language Resources and Evaluation. 3044–3051.28 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.