• Corpus ID: 222066588

Development of Word Embeddings for Uzbek Language

  title={Development of Word Embeddings for Uzbek Language},
  author={B. Mansurov and A. Mansurov},
In this paper, we share the process of developing word embeddings for the Cyrillic variant of the Uzbek language. The result of our work is the first publicly available set of word vectors trained on the word2vec, GloVe, and fastText algorithms using a high-quality web crawl corpus developed in-house. The developed word embeddings can be used in many natural language processing downstream tasks. 

UzBERT: pretraining a BERT model for Uzbek

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Word Embedding based Generalized Language Model for Information Retrieval

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Enriching Word Vectors with Subword Information

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A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

300d word2vec (skipgram, hierarchical softmax) embeddings for Cyrillic Uzbek using webcrawl v1 corpus