• Russian Language and Literature
• /
• no.62
• /
• pp.271-298
• /
• 2018

Uzbekistan, which gained its independence after the dissolution of Soviet Union in 1991, was the second most populous federal republic after Russia and Ukraine during the Soviet era. The population and proportion of indigenous people were relatively high, and more than 80% of Uzbek nationals are originated from the indigenous nation. Uzbekistan implemented language policies such as a national language regulation and elimination as well as reduction of the use of Russian language, which has been functioning as a communicative language among the people. Furthermore, the Uzbek government decided to push ahead with the exclusion of Russian language and change of writing system from Cyrillic to Latin, in order to revive the national identity. These polices were considered one of the strongest along with those of Post Soviet Baltic countries. However, the language polices by the Uzbek government were not very successful. In spite of the overwhelming majority of the population of Uzbek nationality, the fact that a large number of people based on various ethnic backgrounds still live in the region, the influx of Russian and Russian speakers since the Soviet era, the influx of Russian politics, economics, society, education systems and mass media, and finally the superior status of Russian language in everyday life make many people use Russian language actively. The social, economic and educational downturns in Uzbekistan cause a large number of people to leave their country. The Russian Federation is the first desirable place for their immigration. In addition, Uzbekistan is a multi-ethnic society including a local entity called the republic of Karakalpakstan, which occupies approximately one third of the territory of Uzbekistan. Despite the expiration of the 20-year merger treaty between Uzbekistan and Karakalpakstan, the Karakalpak people currently insist on the independence, while the Uzbek government has been ignoring it. The ethnic language policy of Uzbekistan does not seem to have a big problem, but a population census that can reveal the ethnic distribution and use of language has not been implemented since the independence of Uzbekistan. The Uzbek government takes a prudent attitude toward the national language policies. Also, the transition to Latin letters, which began in May 1995, should be finalized in September 2005, but Cyrillic is still being used. Thus, Uzbekistan has been promoting the Uzbek language with the strong ethno-linguistic policies since the beginning of independence, but they do not seem to be fully successful.

• Journal of Information Processing Systems
• /
• v.19 no.3
• /
• pp.355-369
• /
• 2023

Since the widespread adoption of deep-learning and related distributed representation, there have been substantial advancements in part-of-speech (POS) tagging for many languages. When training word representations, morphology and shape are typically ignored, as these representations rely primarily on collecting syntactic and semantic aspects of words. However, for tasks like POS tagging, notably in morphologically rich and resource-limited language environments, the intra-word information is essential. In this study, we introduce a deep neural network (DNN) for POS tagging that learns character-level word representations and combines them with general word representations. Using the proposed approach and omitting hand-crafted features, we achieve 90.47%, 80.16%, and 79.32% accuracy on our own dataset for three morphologically rich languages: Uyghur, Uzbek, and Kyrgyz. The experimental results reveal that the presented character-based strategy greatly improves POS tagging performance for several morphologically rich languages (MRL) where character information is significant. Furthermore, when compared to the previously reported state-of-the-art POS tagging results for Turkish on the METU Turkish Treebank dataset, the proposed approach improved on the prior work slightly. As a result, the experimental results indicate that character-based representations outperform word-level representations for MRL performance. Our technique is also robust towards the-out-of-vocabulary issues and performs better on manually edited text.

• Acta Via Serica
• /
• v.5 no.2
• /
• pp.111-140
• /
• 2020

This paper focuses on the emerging patterns of educational mobility and unskilled labor migration from Uzbekistan to Japan and South Korea. Labor migration and educational mobility are becoming the next "horizon" in the expanded relationship between East and Central Asia, powered by several factors, including the efforts by Japan and South Korea to build "original" people-oriented policy engagements with the region and the demand from Central Asian states, such as Uzbekistan, to provide more labor opportunities to their young and growing populations. This paper presents the initial findings of a pilot survey that explores and occasionally compares the experiences of Uzbek migrants to Japan and South Korea, using datasets of face-to-face interviews related to various aspects of life in Japan and South Korea. The interviews were conducted face to face and online (Telegram, Skype, etc.) with 66 migrants and Japanese language school students (whom this paper treats as labor migrants masquerading as students) in Japan from November 2019 to January 2020 as well as online with 30 laborers and students in South Korea from August to September 2020.