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  • Language: Dutch
  • Project: CLARIN-NL
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  • Corpus of Contemporary Dutch

    The Corpus of Contemporary Dutch (Corpus Hedendaags Nederlands (CLARIN)) is a collection of texts consisting of more than 800,000 texts from newspapers, journals, TV News broadcasts and legal materials (1814-2013). The corpus was created by combining the older 5, 27 and 38 million words corpora and the Parole Corpus, supplemented by newspaper texts from NRC and De Standaard (until 2013). In addition, it contains corpus material from Suriname and the Dutch Antilles.
    Corpus Hedendaags Nederlands (CLARIN) is een tekstverzameling van meer dan 800.000 teksten uit kranten, tijdschriften, journaaluitzendingen en juridisch materiaal (1814-2013). Het corpus is een samenvoeging van het oude 5, 27 en 38 Miljoen Woorden Corpus en het PAROLE Corpus, aangevuld met krantenteksten uit NRC en De Standaard (tot 2013). Daarnaast bevat het corpus materiaal uit Suriname en de Antillen.

  • Frog: An advanced Natural Language Processing Suite for Dutch (Web Service and Application)

    Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. It performs automatic linguistic enrichment such as part of speech tagging, lemmatisation, named entity recognition, shallow parsing, dependency parsing and morphological analysis. All NLP modules are based on TiMBL.
    Iris Hendrickx, Antal van den Bosch, Maarten van Gompel, Ko van der Sloot and Walter Daelemans. 2016.Frog: A Natural Language Processing Suite for Dutch. CLST Technical Report 16-02, pp 99-114. Nijmegen, the Netherlands. https://github.com/LanguageMachines/frog/blob/master/docs/frogmanual.pdf
    Van den Bosch, A., Busser, G.J., Daelemans, W., and Canisius, S. (2007). An efficient memory-based morphosyntactic tagger and parser for Dutch, In F. van Eynde, P. Dirix, I. Schuurman, and V. Vandeghinste (Eds.), Selected Papers of the 17th Computational Linguistics in the Netherlands Meeting, Leuven, Belgium, pp. 99-114. http://ilk.uvt.nl/downloads/pub/papers/tadpole-final.pdf
    Frog (plain text input)
    Frog (folia+xml input)

  • Nederlab, online laboratory for humanities research on Dutch text collections

    The Nederlab project aims to bring together all digitized texts relevant to Dutch national heritage, the history of Dutch language and culture (c. 800 - present) in one user-friendly and tool-enriched open access web interface, allowing scholars to simultaneously search and analyze data from texts spanning the full recorded history of the Netherlands, its language and culture. The project builds on various initiatives: for corpora Nederlab collaborates with the scientific libraries and institutions, for infrastructure with CLARIN (and CLARIAH), for tools with eHumanities programmes such as Catch, IMPACT and CLARIN (TICCL, frog). Nederlab will offer a large number of search options with which researchers can find the occurrence of a particular term in a particular corpus or subcorpus. It'll also offer visualization of search results through line graphs, bar graphs, circle graphs, or scatter graphs. Furthermore, this online lab will offer a large set of tools, like tokenization tools, tools for spelling normalization, PoS-tagging tools, lemmatization tools, a computational historical lexicon and indices. Also, the use of (semi-) automatic syntactic parsing, tools for text mining, data mining and sentiment mining, Named Entity Recognition tools, coreference resolution tools, plagiarism detection tools, paraphrase detection tools and cartographical tools is offered The first version of Nederlab was launched in early 2015, it’ll be expanded until the end of 2017. Nederlab is financed by NWO, KNAW, CLARIAH and CLARIN-NL.
    http://www.nederlab.nl/wp/?page_id=12

  • LASSY Word Relations Search Web Application

    The LASSY word relations web application makes it possible to search for sentences that contain pairs of words between which there is a grammatical relation. One can search in the Dutch LASSY-SMALL Treebank (1 million tokens), in which the syntactic parse of each sentence has been manually verified, and in (a part of) the LASSY-LARGE Treebank (700 million tokens ),in which the syntactic parse of each sentence has been added by the automatic parser Alpino. One can restrict the query to search for words of a particular Part-of-Speech, which is very useful in the case of syntactic ambiguities. One can also leave out the string of the word, so that one can obtain e.g. a list of sentences in which any adverb modifies a given verb, or even any word modifies a given verb. On the page that lists the found sentences one can view the exact syntactic structure of each sentence by a simple click. The application also provides detailed frequency information of all found sentences and word pairs. The Lassy treebanks have been made by the KU Leuven and the Rijksuniversiteit Groningen through financing of the Dutch Language Union. One can obtain these treebanks through the HLT Agency (TST-Centrale). Use PaQu (http://dev.clarin.nl/node/4182) for many more options and if you want to search for word pairs in your own text corpus.

  • PICCL: Philosophical Integrator of Computational and Corpus Libraries

    PICCL is a set of workflows for corpus building through OCR, post-correction, modernization of historic language and Natural Language Processing. It combines Tesseract Optical Character Recognition, TICCL functionality and Frog functionality in a single pipeline. Tesseract offers Open Source software for optical character recognition. TICCL (Text Induced Corpus Clean-up) is a system that is designed to search a corpus for all existing variants of (potentially) all words occurring in the corpus. This corpus can be one text, or several, in one or more directories, located on one or more machines. TICCL creates word frequency lists, listing for each word type how often the word occurs in the corpus. These frequencies of the normalized word forms are the sum of the frequencies of the actual word forms found in the corpus. TICCL is a system that is intended to detect and correct typographical errors (misprints) and OCR errors (optical character recognition) in texts. When books or other texts are scanned from paper by a machine, that then turns these scans, i.e. images, into digital text files, errors occur. For instance, the letter combination `in' can be read as `m', and so the word `regeering' is incorrectly reproduced as `regeermg'. TICCL can be used to detect these errors and to suggest a correct form. Frog enriches textual documents with various linguistic annotations.
    Martin Reynaert, Maarten van Gompel, Ko van der Sloot and Antal van den Bosch. 2015. PICCL: Philosophical Integrator of Computational and Corpus Libraries. Proceedings of CLARIN Annual Conference 2015, pp. 75-79. Wrocław, Poland. http://www.nederlab.nl/cms/wp-content/uploads/2015/10/Reynaert_PICCL-Philosophical-Integrator-of-Computational-and-Corpus-Libraries.pdf
    PICCL

  • Ucto Tokeniser

    Ucto tokenizes text files: it separates words from punctuation, and splits sentences. This is one of the first tasks for almost any Natural Language Processing application. Ucto offers several other basic preprocessing steps such as changing case that you can all use to make your text suited for further processing such as indexing, part-of-speech tagging, or machine translation. The tokeniser engine is language independent. By supplying language-specific tokenisation rules in an external configuration file a tokeniser can be created for a specific language. Ucto comes with tokenization rules for English, Dutch, French, Italian, and Swedish; it is easily extendible to other languages. It recognizes dates, times, units, currencies, abbreviations. It recognizes paired quote spans, sentences, and paragraphs. It produces UTF8 encoding and NFC output normalization, optionally accepts other encodings as input. Optional conversion to all lowercase or uppercase. Ucto supports FoLiA XML.
    Ucto

  • Frog: An advanced Natural Language Processing suite for Dutch

    Frog's current version will tokenize, tag, lemmatize, and morphologically segment word tokens in Dutch text files, will assign a dependency graph to each sentence, will identify the base phrase chunks in the sentence, and will attempt to find and label all named entities.
    Van den Bosch, A., Busser, G.J., Daelemans, W., and Canisius, S. (2007). An efficient memory-based morphosyntactic tagger and parser for Dutch, In F. van Eynde, P. Dirix, I. Schuurman, and V. Vandeghinste (Eds.), Selected Papers of the 17th Computational Linguistics in the Netherlands Meeting, Leuven, Belgium, pp. 99-114

  • COAVA: Cognition, Acquisition and Variation Tool

    In COAVA two sets of databases are made available in a standardized way: one with historical dialect data (the databases WBD and WLD with lexical data of the Brabantish and Limburgian dialect between 1880-1980) and one with first language acquisition data (four databases form the CHILDES project). The databases contain linguistic information (dialect form, standardised form (“Dutchified”), lexical meaning), geographical information (locality, dialect area, province) and information on the source (inquiry forms or monotopic dictionaries and the date of documentation). The visualisation of the first two sets of information will lead to lexical maps. The most typical way for the user to get to the data will be with the use of the browsable concept taxonomy. The databases are, in other words, approachable via search tools but also via a thematic taxonomy. This taxonomy was developed for the dialect databases and covers the general vocabulary. COAVA (COgnition, Acquisition and VAriation Tool) brings together two strange bedfellows: first language acquisition and historical dialectology. In historical linguistics there is the common assumption that language change in the past is due to the process of non-target like transmission of linguistic features between generations i.e. between parents and children. Despite this assumption, both disciplines remain isolated from each other due to, among others, different methods of data-collection and different types of resources with empirical data. The aim of the COAVA project was to demonstrate that the common assumption in historical linguistics, mentioned above, can be examined in detail with the help of Digital Humanities. This interdisciplinary research targets at the development of a tool for easily exploring the linguistic characteristics of concepts. In COAVA two sets of databases are made available in a standardized way: one with historical dialect data (the databases WBD and WLD with lexical data of the Brabantish and Limburgian dialect between 1880-1980) and one with first language acquisition data (four databases form the CHILDES project).
    Leonie Cornips, Jos Swanenberg, Wilbert Heeringa, Folkert de Vriend (2016). The relationship between first language acquisition and dialect variation: Linking resources from distinct disciplines in a CLARIN-NL project. Lingua, Vol. 178, 07.2016, p. 32-45. doi:10.1016/j.lingua.2015.11.007
    Cornips, L., Swanenberg, J., Vriend, F. de, Heeringa, W. (2012), Is what we have acquired early, less vulnerable to variation? A comparison between data from dialectlexicography and data from first language acquisition. http://www.meertens.knaw.nl/coavasite/wp-content/uploads/2012/10/Abstract-SIDG-2-JS.pdf
    Cornips, L., Kemps-Snijders, M., Snijders, M., Swanenberg, J. and Vriend, F. de (2011). Bridging the Gap between First Language Acquisition and Historical Dialectology with the Help of Digital Humanities. SDH Copenhagen. http://www.meertens.knaw.nl/coavasite/wp-content/uploads/2011/11/Paper-SDH.pdf

  • Namescape Search

    Searching and visualizing Named Entities in modern Dutch novels. The named entity (NE) tagging and resolution in NameScape enables quantitative and repeatable research where previously only guesswork and anecdotal evidence was feasible. The visualisation module enables researchers with a less technical background to draw conclusions about functions of names in literary work and help them to explore the material in search of more interesting questions (and answers). Users from other communities (sociolinguistics, sentiment analysis, …) also benefit from the NE tagged data, especially since the NE recognizer is available as a web service, enabling researchers to annotate their own research data. Datasets in NameScape (total of 1.129 books): Corpus Sanders: A corpus of 582 Dutch novels written and published between 1970 and 2009 will. Corpus Huygens: Consists of 22 novels manually tagged with detailed named entity information. IPR for this corpus do not allow distribution. Corpus eBooks: Consists of 7000+ Dutch eBooks tagged automatically with basic NER features and person name Part information. IPR for this corpus do not allow distribution. Corpus SoNaR Books: 105 Dutch books; NE tagged. Corpus Gutenberg Dutch: Consists of 530 NE tagged TEI files converted from the Epub versions of the corresponding Gutenberg documents. Recent research has conclusively proven names in literary works can only be put fully into perspective when studied in a wider context (landscape) of names either in the same text or in related material (the onymic landscape or “namescape”). Research on large corpora is needed to gain a better understanding of e.g. what is characteristic for a certain period, genre, author or cultural region. The data necessary for research on this scale simply does not exist yet. NameScape aims to fill the need by providing a substantial amount of literary works annotated with a rich tag set, thereby enabling researchers to perform their research in more depth than previously possible. Several exploratory visualization tools help the scholar to answer old questions and uncover many more new ones, which can be addressed using the demonstrator.
    de Does, J, Depuydt, K, van Dalen-Oskam, K and Marx, M. 2017. Namescape: Named Entity Recognition from a Literary Perspective. In: Odijk, J and van Hessen, A. (eds.) CLARIN in the Low Countries, Pp. 361–370. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.30. License: CC-BY 4.0
    Karina van Dalen-Oskam (2013), Nordic Noir: a background check on Inspector Van Veeteren, 31 May 2012, http://blog.namescape.nl/?p=47