Work

Uzbek 65 million web corpus lemmatized Word2Vec model

Year: 2026

Type: dataset

Abstract: The model can be used for efficient text representation via Word embeddings (vector representations). A simple Python script for loading and using the model. Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. The Word2Vec implementation used to produce the model, which can later be used for the model, was Gensim. The original corpus was lemmatized using UzbekLemma lemmatizer: https://pypi.org/project/UzbekLemma/.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19481030

Authors: Khajibaeva, Surayyo, Madatov, Khabibula, Vičič, Jernej

Institutions: Urgench State University, University of Primorska, University of Nova Gorica, Research Centre of the Slovenian Academy of Sciences and Arts

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7152503058

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