--- title: "Who cites UDPipe: Trainable Pipeline for Processing…" description: "128 papers of the Elamur Research library cite «UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis…" canonical: "https://research.elamur.ai/free/en/papers/straka-2016-udpipe-trainable-pipeline-processing-conll/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/straka-2016-udpipe-trainable-pipeline-processing-conll/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [UDPipe: Trainable Pipeline for Processing CoNLL-U…](https://md.research.elamur.ai/free/en/papers/straka-2016-udpipe-trainable-pipeline-processing-conll/) / cited by # Who cites «UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing» 128 papers of our library cite it (of the 140,884 whose bibliographies we read) · 18 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/straka-2016-udpipe-trainable-pipeline-processing-conll/) 1. [**Twitter Universal Dependency Parsing for African-American and Mainstream American English**Su Lin Blodgett, Johnny Wei, Brendan O'Connor*2018 · 92 citations*](https://md.research.elamur.ai/free/en/papers/blodgett-2018-twitter-universal-dependency-parsing-african/) 2. [**HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition**Avihay Chriqui, Inbal Yahav*2021 · 68 citations*](https://md.research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/) 3. [**Udapi: Universal API for Universal Dependencies**Martin Popel, Zdeněk Žabokrtský, Martin Vojtek*2017 · 61 citations*](https://md.research.elamur.ai/free/en/papers/popel-2017-udapi-universal-api-universal-dependencies/) 4. [**Overview of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies**Gosse Bouma, Djamé Seddah, Daniel Zeman*2020 · 52 citations*](https://md.research.elamur.ai/free/en/papers/bouma-2020-overview-iwpt-2020-shared-task/) 5. [**'IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks'**Anders Björkelund, Agnieszka Falenska, Xiang Yu, et al.*2017 · 39 citations*](https://md.research.elamur.ai/free/en/papers/bjorkelund-2017-ims-conll-2017-ud-shared/) 6. [**From Raw Text to Enhanced Universal Dependencies: The Parsing Shared Task at IWPT 2021**Gosse Bouma, Djamé Seddah, Daniel Zeman*2021 · 30 citations*](https://md.research.elamur.ai/free/en/papers/bouma-2021-raw-text-enhanced-universal-dependencies/) 7. [**Classifying Syntactic Errors in Learner Language**Leshem Choshen, Dmitry Nikolaev, Yevgeni Berzak, et al.*2020 · 30 citations*](https://md.research.elamur.ai/free/en/papers/choshen-2020-classifying-syntactic-errors-learner-language/) 8. [**Universal Dependencies and Quantitative Typological Trends. A Case Study on Word Order**Chiara Alzetta, Felice Dell'Orletta, Simonetta Montemagni, et al.*2018 · 14 citations*](https://md.research.elamur.ai/free/en/papers/alzetta-2018-universal-dependencies-quantitative-typological-trends/) 9. [**Projection-based Coreference Resolution Using Deep Syntax**Michal Novák, Anna Nedoluzhko, Zdeněk Žabokrtský*2017 · 14 citations*](https://md.research.elamur.ai/free/en/papers/novak-2017-projection-based-coreference-resolution-deep/) 10. [**Yauti: A Tool for Morphosyntactic Analysis of Nheengatu within the Universal Dependencies Framework**Leonel Figueiredo de Alencar*2023 · 7 citations*](https://md.research.elamur.ai/free/en/papers/alencar-2023-yauti-tool-morphosyntactic-analysis-nheengatu/) 11. [**The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages**Lasha Abzianidze, Rik van Noord, Chunliu Wang, et al.*2020 · 7 citations*](https://md.research.elamur.ai/free/en/papers/abzianidze-2020-parallel-meaning-bank-framework-semantically/) 12. [**Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning**Xiang Yu, Ngoc Thang Vu, Jonas Kuhn*2018 · 5 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-approximate-dynamic-oracle-dependency-parsing/) 13. [**Designing the LECOR Learner Corpus for Romanian**Ana Maria Barbu, Elena Irimia, Carmen Mîrzea Vasile, et al.*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/barbu-2023-designing-lecor-learner-corpus-romanian/) 14. [**Minor changes make a difference: a case study on the consistency of UD-based dependency parsers**Dmytro Kalpakchi, Johan Boye*2021 · 2 citations*](https://md.research.elamur.ai/free/en/papers/kalpakchi-2021-minor-changes-make-difference-case/) 15. [**Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: Amharic Dependency Parser**Binyam Ephrem Seyoum, Yusuke Miyao, Baye Yimam Mekonnen*2020 · 2 citations*](https://md.research.elamur.ai/free/en/papers/seyoum-2020-comparing-neural-network-parsers-less/) 16. [**Identifying and Handling Cross-Treebank Inconsistencies in UD: A Pilot Study**Tillmann Dönicke, Xiang Yu, Jonas Kuhn*2020 · 1 citation*](https://md.research.elamur.ai/free/en/papers/donicke-2020-identifying-handling-cross-treebank-inconsistencies/) 17. [**Cross-Dialectal Transfer for Low-Resource Arabic: The Tunisian Arabic Dependency Treebank**Amal Aissaoui*2026*](https://md.research.elamur.ai/free/en/papers/aissaoui-2026-cross-dialectal-transfer-low-resource/) 18. [**LegalSim-PT: Building a Dataset for Legal Document Simplification in Portuguese Leveraging Linguistic Metrics**Arthur Scalercio, Maria José Finatto, Aline Paes*2026*](https://md.research.elamur.ai/free/en/papers/scalercio-2026-legalsim-pt-building-dataset-legal/) 110 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/straka-2016-udpipe-trainable-pipeline-processing-conll/) — UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing