This map shows the geographic impact of Daniel Beck'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 Daniel Beck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Beck more than expected).
This network shows the impact of papers produced by Daniel Beck. 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 Daniel Beck. The network helps show where Daniel Beck may publish in the future.
Co-authorship network of co-authors of Daniel Beck
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Beck.
A scholar is included among the top collaborators of Daniel Beck 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 Daniel Beck. Daniel Beck is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Moss, Henry B., David S. Leslie, Daniel Beck, Javier González, & Paul Rayson. (2020). BOSS: Bayesian Optimization over String Spaces. Lancaster EPrints (Lancaster University). 33. 15476–15486.4 indexed citations
Beck, Daniel. (2017). Modelling Representation Noise in Emotion Analysis using Gaussian Processes. International Joint Conference on Natural Language Processing. 2. 140–145.3 indexed citations
9.
Beck, Daniel & Trevor Cohn. (2017). Learning Kernels over Strings using Gaussian Processes. International Joint Conference on Natural Language Processing. 2. 67–73.3 indexed citations
10.
Scarton, Carolina, et al.. (2016). Word embeddings and discourse information for Machine Translation Quality Estimation. 2.2 indexed citations
Beck, Daniel, Kashif Shah, Trevor Cohn, & Lucia Specia. (2013). SHEF-Lite: When Less is More for Translation Quality Estimation. Workshop on Statistical Machine Translation. 337–342.13 indexed citations
17.
Beck, Daniel, Lucia Specia, & Trevor Cohn. (2013). Reducing Annotation Effort for Quality Estimation via Active Learning. Meeting of the Association for Computational Linguistics. 2. 543–548.4 indexed citations
Beck, Daniel. (2011). Syntax-based Statistical Machine Translation using Tree Automata and Tree Transducers. Meeting of the Association for Computational Linguistics. 36–40.1 indexed citations
20.
Schäfer, Ulrich & Daniel Beck. (2006). Automatic Testing and Evaluation of Multilingual Language Technology Resources and Components. Language Resources and Evaluation. 173–178.2 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.