Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the 'UDPipe' 'NLP' Toolkit

This natural language processing toolkit provides language-agnostic 'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency parsing' of raw text. Next to text parsing, the package also allows you to train annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided at <>. The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at .

This repository contains an R package which is an Rcpp wrapper around the UDPipe C++ library (,

  • UDPipe provides language-agnostic tokenization, tagging, lemmatization and dependency parsing of raw text, which is an essential part in natural language processing.
  • The techniques used are explained in detail in the paper: "Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe", available at In that paper, you'll also find accuracies on different languages and process flow speed (measured in words per second).


The udpipe R package was designed with the following things in mind when building the Rcpp wrapper around the UDPipe C++ library:

  • Give R users simple access in order to easily tokenize, tag, lemmatize or perform dependency parsing on text in any language
  • Provide easy access to pre-trained annotation models
  • Allow R users to easily construct your own annotation model based on data in CONLL-U format as provided in more than 60 treebanks available at
  • Don't rely on Python or Java so that R users can easily install this package without configuration hassle
  • No external R package dependencies except the strict necessary (Rcpp and data.table, no tidyverse)

Installation & License

The package is availabe under the Mozilla Public License Version 2.0. Installation can be done as follows. Please visit the package documentation and package vignette for further details.

vignette("udpipe-tryitout", package = "udpipe")
vignette("udpipe-annotation", package = "udpipe")
vignette("udpipe-train", package = "udpipe")

For installing the development version of this package: devtools::install_github("bnosac/udpipe", build_vignettes = TRUE)


Currently the package allows you to do tokenisation, tagging, lemmatization and dependency parsing with one convenient function called udpipe_annotate

dl <- udpipe_download_model(language = "dutch")

language                                                                      file_model
   dutch C:/Users/Jan/Dropbox/Work/RForgeBNOSAC/BNOSAC/udpipe/dutch-ud-2.0-170801.udpipe

udmodel_dutch <- udpipe_load_model(file = "dutch-ud-2.0-170801.udpipe")
x <- udpipe_annotate(udmodel_dutch, 
                     x = "Ik ging op reis en ik nam mee: mijn laptop, mijn zonnebril en goed humeur.")
x <-
 doc_id paragraph_id sentence_id token_id token lemma  upos                     xpos                                                               feats head_token_id dep_rel deps
   doc1            1           1        1    Ik    ik  PRON        Pron|per|1|ev|nom                          Case=Nom|Number=Sing|Person=1|PronType=Prs             2   nsubj <NA>
   doc1            1           1        2  ging    ga  VERB V|intrans|ovt|1of2of3|ev Aspect=Imp|Mood=Ind|Number=Sing|Subcat=Intr|Tense=Past|VerbForm=Fin             0    root <NA>
   doc1            1           1        3    op    op   ADP                Prep|voor                                                        AdpType=Prep             4    case <NA>
   doc1            1           1        4  reis  reis  NOUN          N|soort|ev|neut                                                         Number=Sing             2     obj <NA>
   doc1            1           1        5    en    en CCONJ               Conj|neven                                                                <NA>             7      cc <NA>
   doc1            1           1        6    ik    ik  PRON        Pron|per|1|ev|nom                          Case=Nom|Number=Sing|Person=1|PronType=Prs             7   nsubj <NA>
   doc1            1           1        7   nam  neem  VERB   V|trans|ovt|1of2of3|ev Aspect=Imp|Mood=Ind|Number=Sing|Subcat=Tran|Tense=Past|VerbForm=Fin             2    conj <NA>

Pre-trained models

Pre-trained Universal Dependencies 2.0 models on all UD treebanks are made available for more than 50 languages, namely:

afrikaans, ancient_greek-proiel, ancient_greek, arabic, basque, belarusian, bulgarian, catalan, chinese, coptic, croatian, czech-cac, czech-cltt, czech, danish, dutch-lassysmall, dutch, english-lines, english-partut, english, estonian, finnish-ftb, finnish, french-partut, french-sequoia, french, galician-treegal, galician, german, gothic, greek, hebrew, hindi, hungarian, indonesian, irish, italian, japanese, kazakh, korean, latin-ittb, latin-proiel, latin, latvian, lithuanian, norwegian-bokmaal, norwegian-nynorsk, old_church_slavonic, persian, polish, portuguese-br, portuguese, romanian, russian-syntagrus, russian, sanskrit, serbian, slovak, slovenian-sst, slovenian, spanish-ancora, spanish, swedish-lines, swedish, tamil, turkish, ukrainian, urdu, uyghur, vietnamese.

These have been made available easily to users of the package by using udpipe_download_model

Train your own models based on CONLL-U data

The package also allows you to build your own annotation model. For this, you need to provide data in CONLL-U format. These are provided for many languages at, mostly under the CC-BY-SA license. How this is done is detailed in the package vignette.

vignette("udpipe-train", package = "udpipe")

Support in text mining

Need support in text mining? Contact BNOSAC:



  • Add docusaurus site
  • udpipe_download_model gains and extra argument called udpipe_model_repo to allow to download models mainly released under CC-BY-SA from
  • Add udpipe_accuracy
  • Add dtm_rbind and dtm_cbind
  • Add udpipe_read_conllu to simplify creating wordvectors
  • Allow to provide several fields in document_term_frequencies to easily allow to include bigrams/trigrams/... for topic modelling purposes e.g. alongside the textrank package or alongside collocation
  • Adding Serbian + Afrikaans
  • Fixing UBSAN messages (misaligned addresses)
  • If user has R version < 3.3.0, use own startsWith function instead of base::startsWith


  • Another stab at fixing the Solaris compilation issue in ufal::udpipe::multiword_splitter::append_token


  • Added phrases to extract POS sequences more easily like noun phrases, verb phrases or any sequence of parts of speech tags and their corresponding words
  • Fix issue in txt_nextgram if n was larger than the number of elements in x
  • Fix heap-use-after-free address sanitiser issue
  • Fix runtime error: null pointer passed as argument 1, which is declared to never be null (e.g. udpipe.cpp: 3338)
  • Another stab at the Solaris compilation issue


  • Added data preparation elements for standard text mining flows namely: cooccurrence collocation document_term_frequencies document_term_matrix dtm_tfidf dtm_remove_terms dtm_remove_lowfreq dtm_remove_tfidf dtm_reverse dtm_cor txt_collapse txt_sample txt_show txt_highlight txt_recode txt_previous txt_next txt_nextgram unique_identifier
  • Added predict.LDA_VEM and predict.LDA_Gibbs
  • Renamed dataset annotation_params to udpipe_annotation_params
  • Added example datasets called brussels_listings, brussels_reviews, brussels_reviews_anno
  • Use path.expand on conll-u files which are used for training
  • udpipe_download_model now downloads from instead of


  • Remove logic of UDPIPE_PROCESS_LOG (using Rcpp::Rout instead). This fixes issue detected with valgrind about ofstream


  • Fix issue on Solaris builds at CRAN, namely: error: expected primary-expression before ‘enum’
  • Use ufal::udpipe namespace directly
  • Documentation fixes


  • Initial release based on UDPipe commit a2ebb99d243546f64c95d0faf36882bb1d67a670
  • Allow to do annotation (tokenisation, POS tagging, Lemmatisation, Dependency parsing)
  • Allow to build your own UDPipe model based on data in CONLL-U format
  • Convert the output of udpipe_annotate to a data.frame
  • Allow to download models from
  • Add vignettes

Reference manual

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0.6 by Jan Wijffels, a month ago,

Browse source code at

Authors: Jan Wijffels [aut, cre, cph], BNOSAC [cph], Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic [cph], Milan Straka [cph], Jana Straková [cph]

Documentation:   PDF Manual  

Task views: Natural Language Processing

MPL-2.0 license

Imports Rcpp, data.table, Matrix, methods

Suggests knitr, topicmodels, lattice

Linking to Rcpp

System requirements: C++11

Imported by corpustools.

Suggested by cleanNLP.

See at CRAN