nlTenTen: Corpus of the Dutch Web

The Dutch Web Corpus (nlTenTen) is a Dutch corpus made up of texts collected from the Internet. The corpus belongs to the TenTen corpus family which is a set of web corpora built using the same method with a target size 10+ billion words. Sketch Engine currently provides access to TenTen corpora in more than 40 languages. Detailed information about TenTen corpora is on the separate page Common TenTen corpora attributes.

Data was crawled by the SpiderLing web spider in June and July 2020. The Dutch corpus 2020 is comprised of 5.9 billion words.

The most recent version of the nlTenTen corpus consists of 5.9 billion words. The texts were downloaded in June and July 2020. The sample texts of the biggest web domains which account for 50% of all corpus texts were checked semi-manually and content with poor quality text and spam was removed.

Part-of-speech tagset

The nlTenTen corpus was POS annotated by TreeTagger using the following NLWAC POS tagset.

Overview of Dutch TenTen corpora

This is a list of Dutch Web corpora available in Sketch Engine:

  • Dutch Web corpus 2020 (nlTenTen20) – 5.9 billion words, genre annotation and topic classification
  • Dutch Web corpus 2014 (nlTenTen17) – 2.2 billion words

Genre annotation and topic classification

A part of the Dutch Web 2020 corpus contains genre annotation and topic classification. These can be displayed as corpus structures in Concordance or in theText type Analysis tool. Genres refer to writing styles and are divided into three groups (blog, discussion, legal) whereas topic classification is inspired by categories used by https://curlie.org/ (formerly dmoz.org) and includes the following topics: arts, business, games, health, home, recreation, reference, science, sport, society, and technology.

  • genres cover 2.8% of the corpus, i.e. 1.88 million tokens
  • topic classification covers 13.3% of the corpus, i.e. 910 million tokens

Hover over the chart to display a number of tokens of the particular topic.

Basic frequency statistics of the Dutch Web 2020 corpus

Tokens 6,836,979,371
Words 5,890,009,964
Sentences 390,258,917
Web pages 16,542,383

Tools to work with the Dutch Web corpus

A complete set of Sketch Engine tools is available to work with this Dutch corpus to generate:

  • word sketch – Dutch collocations categorized by grammatical relations
  • thesaurus – synonyms and similar words for every word
  • keywords – terminology extraction of one-word and multi-word units
  • word lists – lists of Dutch nouns, verbs, adjectives etc. organized by frequency
  • n-grams – frequency list of multi-word units
  • concordance – examples in context
  • text type analysis – statistics of metadata in the corpus

nlTenTen20 (August 2022) version

  • initial size 7,224,506,955 tokens
  • TreeTagger pipeline version 3
  • genre and topic classification
  • the sample texts of the biggest web domains were checked semi-manually and content with poor quality text and spam was removed

nlTenTen14 (9 June 2014) version 1

  • nlTenTen14 – 3.0 billion tokens
  • crawled by SpiderLing in March 2014

TenTen corpora

SUCHOMEL, Vít. Better Web Corpora For Corpus Linguistics And NLP. 2020. Available also from: https://is.muni.cz/th/u4rmz/. Doctoral thesis. Masaryk University, Faculty of Informatics, Brno. Supervised by Pavel RYCHLÝ.

Jakubíček, M., Kilgarriff, A., Kovář, V., Rychlý, P., & Suchomel, V. (2013, July). The TenTen corpus family. In 7th International Corpus Linguistics Conference CL (pp. 125-127).

Suchomel, V., & Pomikálek, J. (2012). Efficient web crawling for large text corpora. In Proceedings of the seventh Web as Corpus Workshop (WAC7) (pp. 39-43).

Genre annotation

SUCHOMEL, Vít. Genre Annotation of Web Corpora: Scheme and Issues. In Kohei Arai, Supriya Kapoor, Rahul Bhatia. Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1. Vancouver, Canada: Springer Nature Switzerland AG, 2021. s. 738-754. ISBN 978-3-030-63127-7. doi:10.1007/978-3-030-63128-4_55.

Search the Dutch corpus

Sketch Engine offers a range of tools to work with this Dutch corpus.

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