Daniel Hershcovich

1.5k total citations
54 papers, 646 citations indexed

About

Daniel Hershcovich is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Communication. According to data from OpenAlex, Daniel Hershcovich has authored 54 papers receiving a total of 646 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Communication. Recurrent topics in Daniel Hershcovich's work include Topic Modeling (37 papers), Natural Language Processing Techniques (33 papers) and Semantic Web and Ontologies (7 papers). Daniel Hershcovich is often cited by papers focused on Topic Modeling (37 papers), Natural Language Processing Techniques (33 papers) and Semantic Web and Ontologies (7 papers). Daniel Hershcovich collaborates with scholars based in Denmark, United States and Israel. Daniel Hershcovich's co-authors include Noam Slonim, Omri Abend, Ehud Aharoni, Ran Levy, Ari Rappoport, Yonatan Bilu, Anders Søgaard, Yong Cao, Laura Cabello and Dan Gutfreund and has published in prestigious journals such as Information Processing & Management, Transactions of the Association for Computational Linguistics and KI - Künstliche Intelligenz.

In The Last Decade

Daniel Hershcovich

49 papers receiving 568 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Hershcovich Denmark 12 570 103 52 32 28 54 646
Jonathan Herzig Israel 13 601 1.1× 67 0.7× 151 2.9× 26 0.8× 32 1.1× 25 726
Rodrigo Agerri Spain 14 462 0.8× 58 0.6× 26 0.5× 16 0.5× 37 1.3× 47 536
Yun-Hsuan Sung United States 10 596 1.0× 64 0.6× 116 2.2× 14 0.4× 32 1.1× 20 695
Junyi Jessy Li United States 15 421 0.7× 135 1.3× 33 0.6× 9 0.3× 17 0.6× 49 550
Ruihong Huang United States 15 876 1.5× 143 1.4× 69 1.3× 34 1.1× 39 1.4× 58 947
Marianna Apidianaki France 11 1.1k 1.8× 105 1.0× 55 1.1× 18 0.6× 60 2.1× 42 1.1k
Gustavo Hernández Ábrego United States 6 473 0.8× 68 0.7× 129 2.5× 9 0.3× 33 1.2× 8 572
Emiel van Miltenburg Netherlands 11 386 0.7× 40 0.4× 133 2.6× 25 0.8× 15 0.5× 29 494
Ranjan Satapathy Singapore 11 315 0.6× 56 0.5× 59 1.1× 53 1.7× 28 1.0× 25 420
Snigdha Chaturvedi United States 14 640 1.1× 85 0.8× 169 3.3× 14 0.4× 28 1.0× 52 802

Countries citing papers authored by Daniel Hershcovich

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Hershcovich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Hershcovich

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Hershcovich. A scholar is included among the top collaborators of Daniel Hershcovich 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 Hershcovich. Daniel Hershcovich 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.
Cao, Yong, et al.. (2025). Towards realistic evaluation of cultural value alignment in large language models: Diversity enhancement for survey response simulation. Information Processing & Management. 62(4). 104099–104099. 1 indexed citations
2.
Zhi, Zhang, et al.. (2025). Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models. Research at the University of Copenhagen (University of Copenhagen). 7592–7608. 1 indexed citations
3.
Cao, Yong, et al.. (2024). Vision-Language Models under Cultural and Inclusive Considerations. 53–66. 1 indexed citations
4.
Hershcovich, Daniel, et al.. (2024). Noise, Novels, Numbers. A Framework for Detecting and Categorizing Noise in Danish and Norwegian Literature. 3344–3354. 1 indexed citations
5.
Hershcovich, Daniel, et al.. (2024). UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause. 5248–5261. 3 indexed citations
6.
Cao, Yong, Li Zhou, Seolhwa Lee, et al.. (2023). Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study. 53–67. 61 indexed citations
7.
Hershcovich, Daniel, et al.. (2023). Probing for Hyperbole in Pre-Trained Language Models. Research at the University of Copenhagen (University of Copenhagen). 200–211. 1 indexed citations
8.
9.
Lee, Seolhwa, et al.. (2023). What does the Failure to Reason with “Respectively” in Zero/Few-Shot Settings Tell Us about Language Models?. Research at the University of Copenhagen (University of Copenhagen). 8786–8800. 1 indexed citations
10.
Cao, Yong, et al.. (2023). Pay More Attention to Relation Exploration for Knowledge Base Question Answering. Research at the University of Copenhagen (University of Copenhagen). 2119–2136.
11.
Søgaard, Anders, Daniel Hershcovich, & Miryam de Lhoneux. (2023). A Two-Sided Discussion of Preregistration of NLP Research. Lirias (KU Leuven). 83–93. 1 indexed citations
12.
Hershcovich, Daniel, Stella Frank, Miryam de Lhoneux, et al.. (2022). Challenges and Strategies in Cross-Cultural NLP. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 6997–7013. 52 indexed citations
13.
López, Hugo A., et al.. (2022). Can AMR Assist Legal and Logical Reasoning?. 1555–1568. 1 indexed citations
14.
Hershcovich, Daniel, et al.. (2022). Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4875–4893. 1 indexed citations
15.
Hansen, Marcus Høy & Daniel Hershcovich. (2022). A Dataset of Sustainable Diet Arguments on Twitter. 40–58. 1 indexed citations
16.
Hartmann, Mareike, et al.. (2021). A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs. Lirias (KU Leuven). 244–257. 8 indexed citations
17.
Abend, Omri, et al.. (2020). Cross-lingual Semantic Representation for NLP with UCCA. OPUS (Augsburg University). 1–9. 2 indexed citations
18.
Oepen, Stephan, Omri Abend, Lasha Abzianidze, et al.. (2020). MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing. Research at the University of Copenhagen (University of Copenhagen). 1–22. 31 indexed citations
19.
Levy, Ran, Yonatan Bilu, Daniel Hershcovich, Ehud Aharoni, & Noam Slonim. (2014). Context Dependent Claim Detection. International Conference on Computational Linguistics. 1489–1500. 100 indexed citations
20.
Slonim, Noam, Ehud Aharoni, Carlos Alzate, et al.. (2014). Claims on demand -- an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora. International Conference on Computational Linguistics. 6–9. 4 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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