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 < http://universaldependencies.org/format.html>. 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 (http://ufal.mff.cuni.cz/udpipe, https://github.com/ufal/udpipe).

• 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 http://ufal.mff.cuni.cz/~straka/papers/2017-conll_udpipe.pdf. In that paper, you'll also find accuracies on different languages and process flow speed (measured in words per second).

General

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 http://universaldependencies.org/#ud-treebanks
• 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 https://bnosac.github.io/udpipe/en and look at the R package vignettes for further details.

install.packages("udpipe")
vignette("udpipe-tryitout", package = "udpipe")
vignette("udpipe-annotation", package = "udpipe")
vignette("udpipe-usecase-postagging-lemmatisation", package = "udpipe")
# An overview of keyword extraction techniques: https://bnosac.github.io/udpipe/docs/doc7.html
vignette("udpipe-usecase-topicmodelling", package = "udpipe")
vignette("udpipe-train", package = "udpipe")

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

Example

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

library(udpipe)
udmodel <- udpipe_download_model(language = "dutch")
udmodel

language                                                                             file_model
   dutch C:/Users/Jan/Dropbox/Work/RForgeBNOSAC/BNOSAC/udpipe/dutch-alpino-ud-2.3-181115.udpipe

x <- udpipe(x = "Ik ging op reis en ik nam mee: mijn laptop, mijn zonnebril en goed humeur.",
            object = udmodel)
x

  doc_id paragraph_id sentence_id start end term_id token_id     token     lemma  upos                     xpos                                                               feats head_token_id      dep_rel deps
   doc1            1           1     1   2       1        1        Ik        ik  PRON        Pron|per|1|ev|nom                          Case=Nom|Number=Sing|Person=1|PronType=Prs             2        nsubj <NA>
   doc1            1           1     4   7       2        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     9  10       3        3        op        op   ADP                Prep|voor                                                        AdpType=Prep             4         case <NA>
   doc1            1           1    12  15       4        4      reis      reis  NOUN          N|soort|ev|neut                                                         Number=Sing             2          obj <NA>
   doc1            1           1    17  18       5        5        en        en CCONJ               Conj|neven                                                                <NA>             7           cc <NA>
   doc1            1           1    20  21       6        6        ik        ik  PRON        Pron|per|1|ev|nom                          Case=Nom|Number=Sing|Person=1|PronType=Prs             7        nsubj <NA>
   doc1            1           1    23  25       7        7       nam      neem  VERB   V|trans|ovt|1of2of3|ev Aspect=Imp|Mood=Ind|Number=Sing|Subcat=Tran|Tense=Past|VerbForm=Fin             2         conj <NA>
   doc1            1           1    27  29       8        8       mee       mee   ADV                Adv|deelv                                                        PartType=Vbp             7 compound:prt <NA>
   doc1            1           1    30  30       9        9         :         : PUNCT            Punc|dubbpunt                                                      PunctType=Colo             2        punct <NA>
...

Pre-trained models

Pre-trained models build on Universal Dependencies treebanks are made available for more than 60 languages based on 90 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, 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, finnish-ftb, finnish-tdt, french-gsd, french-partut, french-sequoia, french-spoken, galician-ctg, galician-treegal, german-gsd, gothic-proiel, greek-gdt, hebrew-htb, hindi-hdtb, hungarian-szeged, indonesian-gsd, irish-idt, italian-isdt, italian-partut, italian-postwita, japanese-gsd, kazakh-ktb, korean-gsd, korean-kaist, kurmanji-mg, latin-ittb, latin-perseus, latin-proiel, latvian-lvtb, lithuanian-hse, maltese-mudt, marathi-ufal, north_sami-giella, norwegian-bokmaal, norwegian-nynorsk, norwegian-nynorsklia, old_church_slavonic-proiel, old_french-srcmf, persian-seraji, polish-lfg, polish-sz, portuguese-bosque, portuguese-br, portuguese-gsd, romanian-nonstandard, romanian-rrt, russian-gsd, russian-syntagrus, russian-taiga, sanskrit-ufal, 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.

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

How good are these models?

• Accuracy statistics of models provided by the UDPipe authors which you download with udpipe_download_model from the default repository are available at this link.
• Accuracy statistics of models trained using this R package which you download with udpipe_download_model from the bnosac/udpipe.models.ud repository are available at https://github.com/bnosac/udpipe.models.ud.
• For a comparison between UDPipe and spaCy visit https://github.com/jwijffels/udpipe-spacy-comparison

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 http://universaldependencies.org/#ud-treebanks, 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: http://www.bnosac.be