Kuzman Ganchev

3.1k total citations
35 papers, 1.4k citations indexed

About

Kuzman Ganchev is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kuzman Ganchev has authored 35 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 6 papers in Molecular Biology and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kuzman Ganchev's work include Topic Modeling (33 papers), Natural Language Processing Techniques (29 papers) and Biomedical Text Mining and Ontologies (6 papers). Kuzman Ganchev is often cited by papers focused on Topic Modeling (33 papers), Natural Language Processing Techniques (29 papers) and Biomedical Text Mining and Ontologies (6 papers). Kuzman Ganchev collaborates with scholars based in United States, Portugal and United Kingdom. Kuzman Ganchev's co-authors include Ben Taskar, Joäo Graça, Jennifer Gillenwater, Dipanjan Das, Oscar Täckström, João Gama, Mark Dredze, Fernando Pereira, Slav Petrov and Ryan McDonald and has published in prestigious journals such as Communications of the ACM, Journal of Machine Learning Research and Computational Linguistics.

In The Last Decade

Kuzman Ganchev

33 papers receiving 1.3k citations

Peers

Countries citing papers authored by Kuzman Ganchev

Since Specialization

Citations

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

Fields of papers citing papers by Kuzman Ganchev

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kuzman Ganchev

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Conditional Generation with a Question-Answering Blueprint 2023 ·Transactions of the Association for Computational Linguistics ·Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, (unknown), Dipanjan Das, Mirella Lapata	12
2	Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation 2023 ·Fantine Huot, Joshua Maynez, Shashi Narayan, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, (unknown), Dipanjan Das, Mirella Lapata	0
3	QAmeleon: Multilingual QA with Only 5 Examples 2023 ·Transactions of the Association for Computational Linguistics ·Priyanka Agrawal, Chris Alberti, Fantine Huot, Joshua Maynez, (unknown), Sebastian Ruder, Kuzman Ganchev, Dipanjan Das, Mirella Lapata	3
4	Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields 2021 ·arXiv (Cornell University) ·Georgi Georgiev, Preslav Nakov, Kuzman Ganchev, Petya Osenova, Kiril Simov	2
5	State-of-the-art Chinese Word Segmentation with Bi-LSTMs 2018 ·Ji Ma, Kuzman Ganchev, David J. Weiss	64
6	Semantic Role Labeling with Neural Network Factors 2015 ·Nicholas FitzGerald, Oscar Täckström, Kuzman Ganchev, Dipanjan Das	70
7	Universal Dependency Annotation for Multilingual Parsing 2013 ·Meeting of the Association for Computational Linguistics ·Ryan McDonald, Joakim Nivre, (unknown), Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar Täckström, (unknown), (unknown), (unknown)	260
8	Cross-Lingual Discriminative Learning of Sequence Models with Posterior Regularization 2013 ·Kuzman Ganchev, Dipanjan Das	25
9	Using Search-Logs to Improve Query Tagging 2012 ·Meeting of the Association for Computational Linguistics ·Kuzman Ganchev, Keith Hall, Ryan McDonald, Slav Petrov	18
10	Multi-View Learning over Structured and Non-Identical Outputs 2012 ·arXiv (Cornell University) ·Kuzman Ganchev, Joäo Graça, John Blitzer, Ben Taskar	21
11	Posterior Sparsity in Unsupervised Dependency Parsing 2011 ·Journal of Machine Learning Research ·Jennifer Gillenwater, Kuzman Ganchev, Joäo Graça, Fernando Pereira, Ben Taskar	26
12	Rich Prior Knowledge in Learning for Natural Language Processing 2011 ·Meeting of the Association for Computational Linguistics ·Gregory Druck, Kuzman Ganchev, Joäo Graça	2
13	Controlling complexity in part-of-speech induction 2011 ·arXiv (Cornell University) ·Joäo Graça, Kuzman Ganchev, Luísa Coheur, Fernando Pereira, Ben Taskar	7
14	Posterior Regularization for Structured Latent Variable Models 2010 ·Journal of Machine Learning Research ·Kuzman Ganchev, Joäo Graça, Jennifer Gillenwater, Ben Taskar	254
15	Sparsity in Dependency Grammar Induction 2010 ·ScholarlyCommons (University of Pennsylvania) ·Jennifer Gillenwater, Kuzman Ganchev, Joäo Graça, Fernando Pereira, Ben Taskar	39
16	Posterior Regularization for Learning with Side Information and Weak Supervision 2010 ·Scholarly Commons (University of Pennsylvania) ·Kuzman Ganchev	0
17	Edlin: an Easy to Read Linear Learning Framework 2009 ·Recent Advances in Natural Language Processing ·Kuzman Ganchev, Georgi Georgiev	1
18	Dependency grammar induction via bitext projection constraints 2009 ·Kuzman Ganchev, Jennifer Gillenwater, Ben Taskar	86
19	Small Statistical Models by Random Feature Mixing 2008 ·Meeting of the Association for Computational Linguistics ·Kuzman Ganchev, Mark Dredze	43
20	Frustratingly Hard Domain Adaptation for Dependency Parsing 2007 ·Empirical Methods in Natural Language Processing ·Mark Dredze, John Blitzer, Partha Talukdar, Kuzman Ganchev, (unknown), Fernando Pereira	57

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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