class="p1">* Диахрония (от греч. δια — через, сквозь и χρονος — время) — рассмотрение исторического развития языковых явлений и языковой системы как предмета лингвистического исследования. Противопоставляется синхронии (от греч. συν — совместно и χρονος — время) — рассмотрение состояния языка как установившейся системы в определённый момент времени.

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Mnih A., Hinton G. E. (2009). A scalable hierarchical distributed language model / Advances in neural information processing systems, Vol. 21, pp. 1081—1088 // https://papers.nips.cc/paper/3583-a-scalable-hierarchical-distributed-language-model

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Mnih A., Teh Y. W. (2012). A fast and simple algorithm for training neural probabilistic language models // Proceedings of the 29th International Coference on International Conference on Machine Learning, pp. 419—426 // https://arxiv.org/abs/1206.6426

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Collobert R., Weston J. (2008). A unified architecture for natural language processing: deep neural networks with multitask learning / Proceedings of the 25th international conference on Machine learning, pp. 160—167 // https://doi.org/10.1145/1390156.1390177

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Turian J., Ratinov L., Bengio Y. (2010). Word representations: a simple and general method for semi-supervised learning / Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384—394 // https://dl.acm.org/doi/10.5555/1858681.1858721

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Mikolov T., Chen K., Corrado G., Dean J. (2013). Efficient Estimation of Word Representations in Vector Space / International Conference on Learning Representations (ICLR-2013) // https://arxiv.org/abs/1301.3781

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Mikolov T., Sutskever I., Chen K., Corrado G., Dean J. (2013). Distributed Representations of Words and Phrases and their Compositionality / Proceedings of the 26th International Conference on Neural Information Processing Systems, Vol. 2, pp. 3111—3119 // https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

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Mikolov T., Sutskever I., Chen K., Corrado G., Dean J. (2013). Distributed Representations of Words and Phrases and their Compositionality / Proceedings of the 26th International Conference on Neural Information Processing Systems, Vol. 2, pp. 3111—3119 // https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

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Mikolov T., Kombrink S., Deoras A., Burget L, Černocký J. (2011). RNNLM — Recurrent Neural Network Language Modeling Toolkit / Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop, 2011, pp. 1—4 // https://www.fit.vut.cz/research/publication/10087/.en

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Wilson B., Schakel A. M. J. (2015). Controlled Experiments for Word Embeddings // https://arxiv.org/abs/1510.02675

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Rajasekharan A. (2017). How does word2vec work? Can someone walk through a specific example? / Quora // https://www.quora.com/How-does-word2vec-work-Can-someone-walk-through-a-specific-example/answer/Ajit-Rajasekharan

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Gong C., He D., Tan X., Qin T., Wang L., Liu T.-Y. (2020). FRAGE: Frequency-Agnostic Word Representation // https://arxiv.org/abs/1809.06858

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Mikolov T., Chen K., Corrado G., Dean J. (2013). Efficient Estimation of Word Representations in Vector Space / International Conference on Learning Representations (ICLR-2013) // https://arxiv.org/abs/1301.3781

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İrsoy O., Benton A., Stratos K. (2020). kōan: A Corrected CBOW Implementation // https://arxiv.org/abs/2012.15332

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Сапунов Г. (2021). kōan: A Corrected CBOW Implementation (Ozan İrsoy, Adrian Benton, Karl Stratos) / gonzo-обзоры ML статей, Jan 19, 2021 // https://t.me/gonzo_ML/452

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* Социальное познание (англ. social cognition) — процесс познания одного человека другим, одна из сфер, изучаемых социальной психологией, которая исследует механизмы хранения, переработки и использования человеком информации о других людях и социальных ситуациях.

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** Организационное поведение (англ. organizational behavior) — научная дисциплина, занимающаяся исследованием поведения людей в организациях.

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Richie R., Zou W., Bhatia S., Vazire S. (2019). Predicting High-Level Human Judgment Across Diverse Behavioral Domains / Psychology, Vol. 5, Iss. 1, p. 50 // https://doi.org/10.1525/collabra.282

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Baroni M., Dinu G., Kruszewski G. (2014). Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors / Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) // https://doi.org/10.3115/v1/P14-1023

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Sivakumar S., Videla L. S., Rajesh Kumar T., Nagaraj J., Itnal S., Haritha D. (2020). Review on Word2Vec Word Embedding Neural Net / 2020 International Conference on Smart Electronics and Communication (ICOSEC) // https://doi.org/10.1109/icosec49089.2020.9215319

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Adewumi T. P., Liwicki F., Liwicki M. (2020). Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks // https://arxiv.org/abs/2003.11645

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Pennington J., Socher R., Manning C. (2014). GloVe: Global Vectors for Word Representation / Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. // https://doi.org/10.3115/v1/D14-1162

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Bojanowski P., Grave E., Joulin A., Mikolov T. (2016). Enriching Word Vectors with Subword Information // https://arxiv.org/abs/1607.04606

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Peters M. E., Neumann M., Iyyer M., Gardner M., Clark C., Lee K., Zettlemoyer L. (2018). Deep contextualized word representations // https://arxiv.org/abs/1802.05365

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Sales J. E., Souza L., Barzegar S., Davis B., Freitas A., Handschuh S. (2018). Indra: A Word Embedding and Semantic Relatedness Server / Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) // https://aclanthology.org/L18-1211/

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Asgari E., Mofrad M. R. K. (2015). ProtVec: A Continuous Distributed Representation of Biological Sequences // https://arxiv.org/abs/1503.05140

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Asgari E., Mofrad M. R. K. (2015). Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics / PLoS One, Vol. 10 (11), e0141287 // https://doi.org/10.1371/journal.pone.0141287

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Jaeger S., Fulle S., Turk S. (2017). Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition. / Journal of Chemical Information and Modeling, Vol. 58. Iss. 1, pp. 27–35. // https://doi.org/10.1021/acs.jcim.7b00616

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Zhang Y.-F., Wang X., Kaushik A.C., Chu Y., Shan X., Zhao M.-Z., Xu Q., Wei D.-Q. (2020). SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction / Frontiers in