mlsanityData to 7 Oct 2026

DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling

arXiv 2602.11968 ↗First posted 12 Feb 2026cs.CLcited by 1

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Abstract (arXiv)

In this resource paper, we present DHPLT, an open collection of diachronic corpora in 41 diverse languages. DHPLT is based on the web-crawled HPLT datasets; we use web crawl timestamps as the approximate signal of document creation time. The collection covers three time periods: 2011-2015, 2020-2021 and 2024-present (1 million documents per time period for each language). We additionally provide pre-computed word type and token embeddings and lexical substitutions for our chosen target words, while at the same time leaving it open for the other researchers to come up with their own target words using the same datasets. DHPLT aims at filling in the current lack of multilingual diachronic corpora for semantic change modelling (beyond a dozen of high-resource languages). It opens the way for a variety of new experimental setups in this field. All the resources described in this paper are available at https://data.hplt-project.org/three/diachronic/, sorted by language.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 12 Feb 2026.

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21 of 29 references.

References · 21

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
  4. 2017Beam Search Strategies for Neural Machine Translationcited by 232
  5. 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
  6. 2020SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detectioncited by 105
  7. 2019Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shiftcited by 37
  8. 2024GPT or BERT: why not both?cited by 26
  9. 2024A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Changecited by 21
  10. 2022Contextualized language models for semantic change detection: lessons learnedcited by 20
  11. 2021Lexical Semantic Change Discoverycited by 13
  12. 2021Three-part diachronic semantic change dataset for Russiancited by 11
9 more
  1. 2019GASC: Genre-Aware Semantic Change for Ancient Greekcited by 10
  2. 2023Substitution-based Semantic Change Detection using Contextual Embeddingscited by 9
  3. 2022Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Changecited by 7
  4. 2023CLASSLA-Stanza: The Next Step for Linguistic Processing of South Slavic Languagescited by 7
  5. 2024Analyzing Semantic Change through Lexical Replacementscited by 7
  6. 2024Definition generation for lexical semantic change detectioncited by 7
  7. 2025Pretraining Language Models for Diachronic Linguistic Change Discoverycited by 4
  8. 2025SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detectioncited by 4
  9. 2024Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distancecited by 1

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  1. 2026ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levelscited by 0