Juri Ganitkevitch

1.7k total citations · 1 hit paper
20 papers, 1.2k citations indexed

About

Juri Ganitkevitch is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Juri Ganitkevitch has authored 20 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Juri Ganitkevitch's work include Natural Language Processing Techniques (20 papers), Topic Modeling (19 papers) and Text Readability and Simplification (7 papers). Juri Ganitkevitch is often cited by papers focused on Natural Language Processing Techniques (20 papers), Topic Modeling (19 papers) and Text Readability and Simplification (7 papers). Juri Ganitkevitch collaborates with scholars based in United States, Germany and United Kingdom. Juri Ganitkevitch's co-authors include Chris Callison-Burch, Benjamin Van Durme, Jonathan Weese, Chris Dyer, Ellie Pavlick, Pushpendre Rastogi, Adam Lopez, Sanjeev Khudanpur, Lane Schwartz and Omar F. Zaidan and has published in prestigious journals such as Language Resources and Evaluation, Edinburgh Research Explorer (University of Edinburgh) and Empirical Methods in Natural Language Processing.

In The Last Decade

Juri Ganitkevitch

20 papers receiving 1.0k citations

Hit Papers

PPDB: The Paraphrase Database 2013 2026 2017 2021 2013 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Juri Ganitkevitch United States 14 1.1k 121 64 61 19 20 1.2k
Alexander Fraser Germany 19 1.4k 1.2× 180 1.5× 115 1.8× 86 1.4× 44 2.3× 73 1.4k
Oier López de Lacalle Spain 16 716 0.6× 79 0.7× 65 1.0× 62 1.0× 6 0.3× 38 765
Christian M. Meyer Germany 12 606 0.5× 70 0.6× 70 1.1× 48 0.8× 33 1.7× 35 655
Abdessamad Echihabi United States 8 742 0.6× 41 0.3× 105 1.6× 51 0.8× 25 1.3× 9 763
Maxime Peyrard Germany 12 557 0.5× 88 0.7× 57 0.9× 32 0.5× 8 0.4× 27 597
Christian Girardi Italy 10 475 0.4× 97 0.8× 55 0.9× 37 0.6× 23 1.2× 19 516
Sam Thomson United States 9 592 0.5× 102 0.8× 45 0.7× 49 0.8× 7 0.4× 19 621
Ann Irvine United States 14 406 0.4× 72 0.6× 32 0.5× 42 0.7× 26 1.4× 23 454
Vishrav Chaudhary United States 13 569 0.5× 163 1.3× 45 0.7× 20 0.3× 10 0.5× 29 603
Josep Crego France 14 741 0.6× 122 1.0× 45 0.7× 44 0.7× 24 1.3× 51 764

Countries citing papers authored by Juri Ganitkevitch

Since Specialization
Citations

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

Fields of papers citing papers by Juri Ganitkevitch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Juri Ganitkevitch

This figure shows the co-authorship network connecting the top 25 collaborators of Juri Ganitkevitch. A scholar is included among the top collaborators of Juri Ganitkevitch 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 Juri Ganitkevitch. Juri Ganitkevitch 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.
Pavlick, Ellie, Pushpendre Rastogi, Juri Ganitkevitch, Benjamin Van Durme, & Chris Callison-Burch. (2015). PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification. 425–430. 171 indexed citations
2.
Pavlick, Ellie, et al.. (2015). Domain-Specific Paraphrase Extraction. 57–62. 7 indexed citations
3.
Ganitkevitch, Juri & Chris Callison-Burch. (2014). The Multilingual Paraphrase Database. Language Resources and Evaluation. 4276–4283. 33 indexed citations
4.
Weese, Jonathan, Juri Ganitkevitch, & Chris Callison-Burch. (2014). PARADIGM: Paraphrase Diagnostics through Grammar Matching. 192–201. 5 indexed citations
5.
Post, Matt, et al.. (2013). Joshua 5.0: Sparser, Better, Faster, Server. Workshop on Statistical Machine Translation. 206–212. 16 indexed citations
6.
Ganitkevitch, Juri, Benjamin Van Durme, & Chris Callison-Burch. (2013). PPDB: The Paraphrase Database. North American Chapter of the Association for Computational Linguistics. 758–764. 400 indexed citations breakdown →
7.
Ganitkevitch, Juri. (2013). Large-Scale Paraphrasing for Natural Language Understanding. 62–68. 4 indexed citations
8.
Ganitkevitch, Juri, Yuan Cao, Jonathan Weese, Matt Post, & Chris Callison-Burch. (2012). Joshua 4.0: Packing, PRO, and Paraphrases. 283–291. 23 indexed citations
9.
Ganitkevitch, Juri, Benjamin Van Durme, & Chris Callison-Burch. (2012). Monolingual Distributional Similarity for Text-to-Text Generation. 256–264. 8 indexed citations
10.
Venugopal, Ashish, Jakob Uszkoreit, David Talbot, Franz Josef Och, & Juri Ganitkevitch. (2011). Watermarking the Outputs of Structured Prediction with an application in Statistical Machine Translation.. Empirical Methods in Natural Language Processing. 1363–1372. 16 indexed citations
11.
Weese, Jonathan, Juri Ganitkevitch, Chris Callison-Burch, Matt Post, & Adam Lopez. (2011). Joshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor. Edinburgh Research Explorer (University of Edinburgh). 478–484. 22 indexed citations
12.
Napoles, Courtney, Chris Callison-Burch, Juri Ganitkevitch, & Benjamin Van Durme. (2011). Paraphrastic Sentence Compression with a Character-based Metric: Tightening without Deletion. 84–90. 18 indexed citations
13.
Ganitkevitch, Juri, Chris Callison-Burch, Courtney Napoles, & Benjamin Van Durme. (2011). Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation. 1168–1179. 44 indexed citations
14.
Zaidan, Omar F. & Juri Ganitkevitch. (2010). An Enriched MT Grammar for Under $100. North American Chapter of the Association for Computational Linguistics. 93–98. 1 indexed citations
15.
Dyer, Chris, Adam Lopez, Juri Ganitkevitch, et al.. (2010). cdec: A Decoder‚ Alignment‚ and Learning framework for finite−state and context−free translation models. Edinburgh Research Explorer (University of Edinburgh). 176 indexed citations
16.
Li, Zhifei, Chris Callison-Burch, Chris Dyer, et al.. (2010). Joshua 2.0: A Toolkit for Parsing-Based Machine Translation with Syntax, Semirings, Discriminative Training and Other Goodies. 133–137. 13 indexed citations
17.
Li, Zhifei, Chris Callison-Burch, Chris Dyer, et al.. (2009). Joshua: An open source toolkit for parsing-based machine translation. 25–28. 60 indexed citations
18.
Li, Zhifei, Chris Callison-Burch, Chris Dyer, et al.. (2009). Demonstration of Joshua. 25–28. 6 indexed citations
19.
Li, Zhifei, Chris Callison-Burch, Chris Dyer, et al.. (2009). Joshua. 135–135. 112 indexed citations
20.
Hasan, Saša, et al.. (2008). Triplet lexicon models for statistical machine translation. 372–372. 32 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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