R package for Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing Based on the UDPipe Natural Language Processing Toolkit
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README.md

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 (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-ud-2.0-170801.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 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

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 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