German Test Data for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

German Test Data for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

German Test Data for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection

Type

Corpus, Dataset

Author

Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi

Description

This data collection contains the German test data for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection:

  • a lemmatized German text corpus pair (corpus1/lemma/, corpus2/lemma/)
  • 48 lemmas (targets) which have been annotated for their lexical semantic change between the two corpora (targets.txt)
  • the annotated binary change scores of the targets for subtask 1, and their annotated graded change scores for subtask 2 (truth/)

Corpus 1 (lemma version)

  • based on: DTA
  • language: German
  • time covered: 1800-1899
  • size: ~70 million tokens
  • format: lemmatized, sentence length > 9 (before removal of punctuation), no punctuation, sentences randomly shuffled
  • encoding: UTF-8

Corpus 2 (lemma version)

  • based on: BZ and ND
  • language: German
  • time covered: 1946-1990
  • size: ~72 million tokens
  • format: lemmatized, sentence length > 9 (before removal of punctuation), no punctuation, sentences randomly shuffled
  • encoding: UTF-8
  • note: contains frequent OCR errors

Besides the official lemma version of the corpora for SemEval-2020 Task 1 we also provide the raw token version (corpus1/token/, corpus2/token/). It contains the raw sentences in the same order as in the lemma version. Find more information on the data and SemEval-2020 Task 1 in the paper referenced below.

The creation of the data was supported by the CRETA center and the CLARIN-D grant funded by the German Ministry for Education and Research (BMBF).

Reference

Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi. 2020. SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. SemEval@COLING2020.

Sabine Schulte im Walde
Apl. Prof. Dr.

Sabine Schulte im Walde

Akademische Rätin (Associate Professor)

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