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UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing
Open accessAbstract
Automatic natural language processing of large texts often presents recurring challenges in multiple languages: even for most advanced tasks, the texts are first processed by basic processing steps -from tokenization to parsing.We present an extremely simple-to-use tool consisting of one binary and one model (per language), which performs these tasks for multiple languages without the need for any other external data.UDPipe, a pipeline processing CoNLL-U-formatted files, performs tokenization, morphological analysis, part-of-speech tagging, lemmatization and dependency parsing for nearly all treebanks of Universal Dependencies 1.2 (namely, the whole pipeline is currently available for 32 out of 37 treebanks).In addition, the pipeline is easily trainable with training data in CoNLL-U format (and in some cases also with additional raw corpora) and requires minimal linguistic knowledge on the users' part.The training code is also released.
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Cited by
Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe
Straka, M., & Straková, J. (2017) · Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies · doi:10.18653/v1/k17-3009 · Cited by 567
Self-citationCoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Zeman, D., Popel, M., Straka, M., Hajic, J., Nivre, J., Ginter, F., Luotolahti, J., Pyysalo, S., Petrov, S., Potthast, M., et al. (2017) · Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies · doi:10.18653/v1/k17-3001 · Cited by 367
Self-citationHow Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
Rust, P., Pfeiffer, J., Vulić, I., Ruder, S., & Gurevych, I. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.243 · Cited by 202
When ELIZA meets therapists: a Turing test for the heart and mind
Hatch, S. G., Goodman, Z. T., Vowels, L., Hatch, H. D., Brown, A. L., Guttman, S., Le, Y., Bailey, B., Bailey, R. J., Esplin, C. R., et al. (2025) · PLOS Mental Health · doi:10.1371/journal.pmen.0000145 · Cited by 73
Metadata
- Authors
- Straka, M., Hajič, J., & Straková, J.
- Venue
- Language Resources and Evaluation Conference (LREC)
- Published
- 2016-05-01
- Pages
- 4290-4297
- OpenAlex
- W2574640638
- Cited by
- 350
- References
- 0
- FWCI
- 30.31
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Straka, M., Hajič, J., & Straková, J. (2016). UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing. Language Resources and Evaluation Conference (LREC). https://doi.org/10.63317/2q6bwb9anobz
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
