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udpipe - R package for Tokenization, Tagging, Lemmatization and Dependency Parsing Based on UDPipe

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 100 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 available under the Mozilla Public License Version 2.0. Installation can be done as follows. Please visit the package documentation at and look at the R package vignettes for further details.

vignette("udpipe-tryitout", package = "udpipe")
vignette("udpipe-annotation", package = "udpipe")
vignette("udpipe-universe", package = "udpipe")
vignette("udpipe-usecase-postagging-lemmatisation", package = "udpipe")
# An overview of keyword extraction techniques:
vignette("udpipe-usecase-topicmodelling", package = "udpipe")
vignette("udpipe-parallel", package = "udpipe")
vignette("udpipe-train", package = "udpipe")

For installing the development version of this package: remotes::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

udmodel <- udpipe_download_model(language = "dutch")

    language                                                                             file_model
dutch-alpino C:/Users/Jan/Dropbox/Work/RForgeBNOSAC/BNOSAC/udpipe/dutch-alpino-ud-2.5-191206.udpipe

x <- udpipe(x = "Ik ging op reis en ik nam mee: mijn laptop, mijn zonnebril en goed humeur.",
            object = udmodel)
 doc_id paragraph_id sentence_id start end term_id token_id     token     lemma  upos                                        xpos                               feats head_token_id      dep_rel            misc
   doc1            1           1     1   2       1        1        Ik        ik  PRON                VNW|pers|pron|nomin|vol|1|ev      Case=Nom|Person=1|PronType=Prs             2        nsubj            <NA>
   doc1            1           1     4   7       2        2      ging      gaan  VERB                               WW|pv|verl|ev Number=Sing|Tense=Past|VerbForm=Fin             0         root            <NA>
   doc1            1           1     9  10       3        3        op        op   ADP                                     VZ|init                                <NA>             4         case            <NA>
   doc1            1           1    12  15       4        4      reis      reis  NOUN                  N|soort|ev|basis|zijd|stan              Gender=Com|Number=Sing             2          obl            <NA>
   doc1            1           1    17  18       5        5        en        en CCONJ                                    VG|neven                                <NA>             7           cc            <NA>
   doc1            1           1    20  21       6        6        ik        ik  PRON                VNW|pers|pron|nomin|vol|1|ev      Case=Nom|Person=1|PronType=Prs             7        nsubj            <NA>
   doc1            1           1    23  25       7        7       nam     nemen  VERB                               WW|pv|verl|ev Number=Sing|Tense=Past|VerbForm=Fin             2         conj            <NA>
   doc1            1           1    27  29       8        8       mee       mee   ADP                                      VZ|fin                                <NA>             7 compound:prt   SpaceAfter=No
   doc1            1           1    30  30       9        9         :         : PUNCT                                         LET                                <NA>             7        punct            <NA>

Pre-trained models

Pre-trained models build on Universal Dependencies treebanks are made available for more than 65 languages based on 101 treebanks, namely:

afrikaans-afribooms, ancient_greek-perseus, ancient_greek-proiel, arabic-padt, armenian-armtdp, basque-bdt, belarusian-hse, bulgarian-btb, buryat-bdt, catalan-ancora, chinese-gsd, chinese-gsdsimp, classical_chinese-kyoto, coptic-scriptorium, croatian-set, czech-cac, czech-cltt, czech-fictree, czech-pdt, danish-ddt, dutch-alpino, dutch-lassysmall, english-ewt, english-gum, english-lines, english-partut, estonian-edt, estonian-ewt, finnish-ftb, finnish-tdt, french-gsd, french-partut, french-sequoia, french-spoken, galician-ctg, galician-treegal, german-gsd, german-hdt, gothic-proiel, greek-gdt, hebrew-htb, hindi-hdtb, hungarian-szeged, indonesian-gsd, irish-idt, italian-isdt, italian-partut, italian-postwita, italian-twittiro, italian-vit, japanese-gsd, kazakh-ktb, korean-gsd, korean-kaist, kurmanji-mg, latin-ittb, latin-perseus, latin-proiel, latvian-lvtb, lithuanian-alksnis, lithuanian-hse, maltese-mudt, marathi-ufal, north_sami-giella, norwegian-bokmaal, norwegian-nynorsk, norwegian-nynorsklia, old_church_slavonic-proiel, old_french-srcmf, old_russian-torot, persian-seraji, polish-lfg, polish-pdb, polish-sz, portuguese-bosque, portuguese-br, portuguese-gsd, romanian-nonstandard, romanian-rrt, russian-gsd, russian-syntagrus, russian-taiga, sanskrit-ufal, scottish_gaelic-arcosg, serbian-set, slovak-snk, slovenian-ssj, slovenian-sst, spanish-ancora, spanish-gsd, swedish-lines, swedish-talbanken, tamil-ttb, telugu-mtg, turkish-imst, ukrainian-iu, upper_sorbian-ufal, urdu-udtb, uyghur-udt, vietnamese-vtb, wolof-wtb.

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

How good are these models?

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: