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Journal articles on the topic 'Uyghur language'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Ponka, Tatyana I., Anastasia E. Shlentova, and Andrey A. Ivashkevich. "Ethnic and cultural issues of Uyghurs identity in Xinjiang region." RUDN Journal of World History 11, no. 1 (December 15, 2019): 34–43. http://dx.doi.org/10.22363/2312-8127-2019-11-1-34-43.

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The Uyghurs are a Turkic-Muslim minority in the People`s Republic of China (PRC), their native language belongs to a Turkic language family and is written on the basis of Arabic graphics, and regard themselves as culturally and ethnically close to Central Asian nations. This article deals with the issue of the Uyghur identity role in the case of the Xinjiang Uygur Autonomous Region (XUAR) in China and its manifestations in relation to Chinese policy in the region. In order to study this issue the article analyzes the Uyghurs` attitude towards the Han Chinese migrants and their reaction towards Mandarin tuition as well as the salience of Islam faith as a crucial identity marker.

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2

ZANG, XIAOWEI. "Major Determinants of Uyghur Ethnic Consciousness in Ürümchi." Modern Asian Studies 47, no. 6 (May 29, 2013): 2046–71. http://dx.doi.org/10.1017/s0026749x12000558.

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AbstractRising Uyghur ethnic consciousness in the post-1978 era is believed to cause tense Uyghur-Han relations and conflicts in Xinjiang. There are different accounts linking rising Uyghur consciousness with variables such as Han migration into Xinjiang, ethnic inequalities, Uyghur language, and Islamic religiosity. Yet there is no concrete effort to summarize, elaborate, and verify these accounts. Nor is there a quantitative study of the levels of Uyghur ethnic consciousness in Xinjiang. Using data from a survey (N = 799) conducted in Ürümchi in 2007, this paper shows a high level of ethnic consciousness among Uyghurs. It also shows that Uyghur consciousness is based more on cultural and psychological properties than on instrumental sentiments.

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3

Yakup, Mahire, and Joan A. Sereno. "Acoustic correlates of lexical stress in Uyghur." Journal of the International Phonetic Association 46, no. 1 (January 20, 2016): 61–77. http://dx.doi.org/10.1017/s0025100315000183.

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The present study examined lexical stress patterns in Uyghur, a Turkic language. The main goal of this research was to isolate and determine which acoustic parameters provide cues to stress in Uyghur. A number of studies have investigated the phonetic correlates of lexical stress across the world's languages, with stressed syllables often longer in duration, higher in pitch, and greater in amplitude. The present study systematically investigated the acoustic cues to stress in Uyghur, examining duration, fundamental frequency, and amplitude. Three experiments were conducted: one utilizing minimal pairs in Uyghur, one examining disyllabic nouns in Uyghur that contrasted in the first syllable, and one investigating the interaction of lexical stress with Uyghur sentence intonation. The data consistently show that duration was a robust cue to stress in Uyghur, with less consistent effects for intensity. The data also clearly show that fundamental frequency was not a cue to lexical stress in Uyghur. Uyghur does not use the fundamental frequency to distinguish stressed from unstressed syllables. The results suggest that Uyghur does not pattern like a pitch-accent language (e.g. Turkish), but rather like a stress-accent language.

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4

Burkitbayeva, Sh, and G. Kortabaeva. "FEATURES OF UİGHUR POETRY IN INDEPENDENT KAZAKHSTAN." BULLETIN Series of Philological Sciences 73, no. 3 (July 15, 2020): 218–25. http://dx.doi.org/10.51889/2020-3.1728-7804.32.

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The article deals with the poetry of the Uyghur diaspora living in independent Kazakhstan. First of all, this work is due to the fact that there are very few scientific works in the Kazakh language about the songs, poets and literary figures of the Uyghurs living in the independent Kazakh land. Second, the study aims to introduce ethnic Uyghur songs from the years of independence. The article also discusses the growth and prosperity of the poetry of the Uyghur diaspora, which was formed as a branch of the literature of independent Kazakhstan, the poets and their works. It is revealed that the main theme of the poetry of the fraternal Uyghur diaspora, one of the Turkic-speaking peoples, is developing in the same direction as the themes of Kazakh literature, and excerpts from his poems are given. The article examines the poetry of the Uyghur diaspora from 1991 to the present, when Kazakhstan gained independence, in three stages. It is analyzed that Uyghur poetry develops and continues as a branch of the literature of Independent Kazakhstan in all three stages, both in terms of themes and genres. The article uses materials from the library of the Uyghur Cultural Center in Almaty.Works from the literary fund of the Department of Uyghur Studies of the Institute of Oriental Studies named after Suleimenov and the poets themselves were used.

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5

Chen, Si, Peizhi Wen, and Chen Chen. "Vocabulary development of young Uyghur children: Exploring trajectories of receptive and expressive vocabulary." Journal of Chinese Writing Systems 4, no. 1 (January 23, 2020): 19–26. http://dx.doi.org/10.1177/2513850219891618.

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Recent researches of Xinjiang Uyghur young children’s bilingual development provide little empirical evidence of the relationship between Uyghur children’s first language (Uyghur) and second language (Mandarin Chinese). This study was designed to explore how children’s Uyghur vocabulary impacts Chinese vocabulary development. Using a randomized sample of 379 Uyghur children aged 4–6 from Urumqi and Turpan city of Xinjiang, we tested children’s Chinese receptive vocabulary, Chinese expressive vocabulary, Uyghur receptive vocabulary, and Uyghur expressive vocabulary. Results of multilevel regression models showed that after controlling for Uyghur children’s age and kindergarten level, their Chinese receptive vocabulary and Chinese expressive vocabulary can be significantly predicted by Uyghur receptive vocabulary. When Uyghur receptive vocabulary increases by 1 standard deviation, children’s Chinese receptive vocabulary will significantly increase by 0.31 standard deviations, while Chinese expressive vocabulary will significantly increase by 0.18 standard deviations after controlling for children’s age and kindergarten level. However, young children’s Uyghur expressive vocabulary cannot predict either Chinese receptive vocabulary or Chinese expressive vocabulary. Path analysis showed that there were significant direct effects from Uyghur receptive vocabulary to Chinese receptive vocabulary and Chinese receptive vocabulary to Chinese expressive vocabulary. Also, there was a significant indirect effect from Uyghur receptive vocabulary via Chinese receptive vocabulary that impacts Chinese expressive vocabulary. We found that Chinese receptive vocabulary was a full mediator and an important pathway from Uyghur children’s first language vocabulary to second language vocabulary. Findings of this study provide empirical evidence for psychological education researchers to create new curricula to improve young Uyghur children’s second language learning.

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6

Gao, Maomao. "An Analysis of Ethnic Influence on Language: Mandarin or Xinjiang Mandarin?" Acta Universitatis Sapientiae, Philologica 10, no. 2 (November 1, 2018): 99–107. http://dx.doi.org/10.2478/ausp-2018-0016.

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AbstractThis paper aims to investigate lexical borrowings from ethnic languages to standard Mandarin. Data are collected through daily observation after years of living in Xinjiang, China. The data suggest that phonetic loans and hybrid loans are the major approaches in lexical borrowings from Uyghur, Russian, and Persian. Two motivations behind Uyghur borrowings into Mandarin are discussed: cultural borrowings and core borrowings. Cultural borrowings are new objects, concepts of ethnic origins, which are new to Han Chinese communities. Core borrowings are Mandarin words which have been replaced by Uyghur. However, core borrowings are not associated with the prestige of the donor language in this case; instead, frequency, marker, friendliness, and religious consideration are the major reasons. In addition, this paper analyses the strategies of lexical borrowings from ethnic languages in Mandarin. It further discusses the cultural backgrounds behind lexical borrowings. The lexical borrowings from ethnic languages into Mandarin suggest the openness and inclusiveness of Chinese language and culture.

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7

Abdureşit, Z. "On the Hıstorıcal Place Names of East Turkıstan Passed ın Hıstorıcal Poems ın Chınese." Turkology 5, no. 103 (October 15, 2020): 59–79. http://dx.doi.org/10.47526/2020/2664-3162.012.

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There are many poems recorded East Turkestan’s place names in Chinese history poems,. In this article, poems in which the historical place names of East Turkistan, such as “Tanridag”, “Altay”, “Küsen” and “Kroran” are mentioned in Chinese historical poems are discussed. By examining the place names of East Turkestan in the historical poems of China, as Uyghur historian Turgun Almas said, these place names are philological proof that the original inhabitants of the Cungar and Tarim basin are Uyghurs. If the people living in these regions were not Uyghurs, but the people who spoke in another language, then the names of these lakes, mountains, places and rivers would not be called “Tanritag”, “Altay”, “Küsen”, “Kroran” in Uyghur Turkish. The recording of the place names of East Turkistan in the historical poems of China is another philological advantage.

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8

Abdureşit, Z. "On the Hıstorıcal Place Names of East Turkıstan Passed ın Hıstorıcal Poems ın Chınese." Turkology 5, no. 103 (October 15, 2020): 59–79. http://dx.doi.org/10.47526/2020/2664-3162.012.

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There are many poems recorded East Turkestan’s place names in Chinese history poems,. In this article, poems in which the historical place names of East Turkistan, such as “Tanridag”, “Altay”, “Küsen” and “Kroran” are mentioned in Chinese historical poems are discussed. By examining the place names of East Turkestan in the historical poems of China, as Uyghur historian Turgun Almas said, these place names are philological proof that the original inhabitants of the Cungar and Tarim basin are Uyghurs. If the people living in these regions were not Uyghurs, but the people who spoke in another language, then the names of these lakes, mountains, places and rivers would not be called “Tanritag”, “Altay”, “Küsen”, “Kroran” in Uyghur Turkish. The recording of the place names of East Turkistan in the historical poems of China is another philological advantage.

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9

Anwar, Azmat, Xiao Li, Yating Yang, and Yajuan Wang. "Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection." Applied Sciences 9, no. 16 (August 13, 2019): 3318. http://dx.doi.org/10.3390/app9163318.

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Although considerable effort has been devoted to building commonsense knowledge bases (CKB), it is still not available for many low-resource languages such as Uyghur because of expensive construction cost. Focusing on this issue, we proposed a cross-lingual knowledge-projection method to construct an Uyghur CKB by projecting ConceptNet’s Chinese facts into Uyghur. We used a Chinese–Uyghur bilingual dictionary to get high-quality entity translation in facts and employed a back-translation method to eliminate the entity-translation ambiguity. Moreover, to tackle the inner relation ambiguity in translated facts, we made a hand-crafted rule to convert the structured facts into natural-language phrases and built the Chinese–Uyghur lingual phrases based on the similarity of phrases that corresponded to the bilingual semantic similarity scoring model. Experimental results show that the accuracy of our semantic similarity scoring model reached 94.75% for our task, and they successfully project 55,872 Chinese facts into Uyghur as well as obtain 67,375 Uyghur facts within a very short period.

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10

Mi, Chenggang, Shaolin Zhu, and Rui Nie. "Improving Loanword Identification in Low-Resource Language with Data Augmentation and Multiple Feature Fusion." Computational Intelligence and Neuroscience 2021 (April 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/9975078.

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Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross-lingual information retrieval, and so on. However, recent studies on this topic usually put efforts on high-resource languages (such as Chinese, English, and Russian); for low-resource languages, such as Uyghur and Mongolian, due to the limitation of resources and lack of annotated data, loanword identification on these languages tends to have lower performance. To overcome this problem, we first propose a lexical constraint-based data augmentation method to generate training data for low-resource language loanword identification; then, a loanword identification model based on a log-linear RNN is introduced to improve the performance of low-resource loanword identification by incorporating features such as word-level embeddings, character-level embeddings, pronunciation similarity, and part-of-speech (POS) into one model. Experimental results on loanword identification in Uyghur (in this study, we mainly focus on Arabic, Chinese, Russian, and Turkish loanwords in Uyghur) showed that our proposed method achieves best performance compared with several strong baseline systems.

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11

ALIMJAN, Aysa, Ibrahim TURGUN, Obul KURBAN, and Zhe LI. "Phrase based Uyghur language text categorization." Journal of Computer Applications 32, no. 10 (May 23, 2013): 2923–26. http://dx.doi.org/10.3724/sp.j.1087.2012.02923.

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12

Munire, Muhetaer, Xiao Li, and Yating Yang. "Construction of the Uyghur Noun Morphological Re-Inflection Model Based on Hybrid Strategy." Applied Sciences 9, no. 4 (February 19, 2019): 722. http://dx.doi.org/10.3390/app9040722.

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In this paper, a hybrid strategy of rules and statistics is employed to implement the Uyghur Noun Re-inflection model. More specifically, completed Uyghur sentences are taken as an input, and these Uyghur sentences are marked with part of speech tagging, and the nouns in the sentences remain the form of the stem. In this model, relevant linguistic rules and statistical algorithms are used to find the most probable noun suffixes and output the Uyghur sentences after the nouns are re-inflected. With rules of linguistics artificially summed up, the training corpora are formed by the human–machine exchange. The final experimental result shows that the Uyghur morphological re-inflection model is of high performance and can be applied to various fields of natural language processing, such as Uyghur machine translation and natural language generation.

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13

Chen, Hua Ying, and Yi Yang. "An Acoustic Experimental Study on Sound Acquisition in Standard Chinese by Native Speakers of Uyghur." Advanced Materials Research 490-495 (March 2012): 2027–31. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.2027.

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The present acoustic experiment studies the acquisition of Standard Chinese vowels by native speakers of Uyghur. The results indicate that in identifying the 7 vowels of Standard Chinese produced by the native Han Chinese speakers, Uyghur subjects with high Chinese proficiency perform better both in accuracy and reaction time than Uyghur subjects with low Chinese proficiency, and all the Uyghur subjects are not good in perceiving vowels //, /i/ and //. The results of the experiment support the Speech Learning Model and the Perceptual Assimilation Model. In addition, it shows that the second language speech acquisition is restricted by the first language phonological system.

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14

Dong, Rui, Yating Yang, and Tonghai Jiang. "Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation." Information 10, no. 6 (June 6, 2019): 202. http://dx.doi.org/10.3390/info10060202.

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This research was conducted to solve the out-of-vocabulary problem caused by Uyghur spelling errors in Uyghur–Chinese machine translation, so as to improve the quality of Uyghur–Chinese machine translation. This paper assesses three spelling correction methods based on machine translation: 1. Using a Bilingual Evaluation Understudy (BLEU) score; 2. Using a Chinese language model; 3. Using a bilingual language model. The best results were achieved in both the spelling correction task and the machine translation task by using the BLEU score for spelling correction. A maximum F1 score of 0.72 was reached for spelling correction, and the translation result increased the BLEU score by 1.97 points, relative to the baseline system. However, the method of using a BLEU score for spelling correction requires the support of a bilingual parallel corpus, which is a supervised method that can be used in corpus pre-processing. Unsupervised spelling correction can be performed by using either a Chinese language model or a bilingual language model. These two methods can be easily extended to other languages, such as Arabic.

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15

Shklovsky, Kirill, and Yasutada Sudo. "The Syntax of Monsters." Linguistic Inquiry 45, no. 3 (July 2014): 381–402. http://dx.doi.org/10.1162/ling_a_00160.

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We present novel data showing that indexicals, first and second person pronouns in particular, occurring in a certain kind of attitude report in Uyghur are interpreted with respect to the reported context (indexical shifting). While previous authors report similar shifted interpretations of indexicals in languages such as Amharic and Zazaki, we observe a unique feature of Uyghur indexical shifting: it is sensitive to structural positions of the indexical item, and as a consequence can be partial. We account for the structural sensitivity of Uyghur indexical shifting with a context-shifting operator (or monster) that is syntactically independent from the embedding attitude predicate.

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16

Nimaiti, Maimitili, and Yamamoto Izumi. "A Rule Based Approach for Japanese-Uyghur Machine Translation System." International Journal of Software Science and Computational Intelligence 6, no. 1 (January 2014): 56–69. http://dx.doi.org/10.4018/ijssci.2014010104.

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Japanese Uyghur machine translation system has been designed and developed using recent rule based approach. Even though Japanese and Uyghur language has many similarities, but there are also some linguistic differences cause serious problems to the word for word translation. In fact, as straightforward word-for-word Japanese-Uighur translation sometimes yields unnatural Uighur sentences. To raise the translation accuracy, the authors propose a word-for-word translation system using subject verb agreement in Uighur. After a brief introduction to the comparative study of Japanese-Uyghur grammars, morphology and syntax, the authors explain their developing of a word to word rule base system. The coverage of this rule base system, the rules for translation, comparison of experimental result between statistical machine translation system and rule base machine translation system are explained. Some practical suffix translation methods solving problems in Uyghur language are also proposed.

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17

Brophy, David. "Taranchis, Kashgaris, and the ‘Uyghur Question’ in Soviet Central Asia." Inner Asia 7, no. 2 (2005): 163–84. http://dx.doi.org/10.1163/146481705793646892.

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AbstractUp till now, the problem of Uyghur identity construction has been studied from an almost exclusively anthropological perspective. Little Western research has been done on the history of the Uyghur community in the Soviet Union during the period of national delimitation, and the process by which a re-invented ‘Uyghur’ identity was fostered among settled Turkicspeakers of East Turkestani origin. In this paper I have set out to trace some of the key events and debates which formed part of that process. In doing so I provide evidence that challenges certain aspects of the standard account of this period, in particular the role of the 1921 Tashkent conference. In 1921 the term ‘Uyghur’ was not used an ethnic designation, but as an umbrella term for various peoples with family roots in Eastern Turkestan. It was not until several years later that the term took its place beside other ethnonyms in the Soviet Union, provoking debate and opposition in the Soviet Uyghur press. This paper is largely based on the recently republished writings of leading Uyghur activists and journalists from the 1920s, and focuses on the role of the Uyghur Communist Abdulla Rozibaqiev. My paper attempts to demonstrate the importance of basing the study of Uyghur history on Uyghur language sources, rather than Russian or Chinese materials alone.

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18

Clothey, Rebecca. "Language, education and Uyghur identity in urban Xinjiang." Frontiers of Education in China 11, no. 4 (December 2016): 543–45. http://dx.doi.org/10.1007/bf03397140.

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19

Saimaiti, Alimu, Lulu Wang, and Tuergen Yibulayin. "Learning Subword Embedding to Improve Uyghur Named-Entity Recognition." Information 10, no. 4 (April 15, 2019): 139. http://dx.doi.org/10.3390/info10040139.

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Uyghur is a morphologically rich and typical agglutinating language, and morphological segmentation affects the performance of Uyghur named-entity recognition (NER). Common Uyghur NER systems use the word sequence as input and rely heavily on feature engineering. However, semantic information cannot be fully learned and will easily suffer from data sparsity arising from morphological processes when only the word sequence is considered. To solve this problem, we provide a neural network architecture employing subword embedding with character embedding based on a bidirectional long short-term memory network with a conditional random field layer. Our experiments show that subword embedding can effectively enhance the performance of the Uyghur NER, and the proposed method outperforms the model-based word sequence method.

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20

Halike, Ayiguli, Kahaerjiang Abiderexiti, and Tuergen Yibulayin. "Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method." Information 11, no. 1 (January 6, 2020): 31. http://dx.doi.org/10.3390/info11010031.

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Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur.

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21

Chen, Siqi, Yijie Pei, Zunwang Ke, and Wushour Silamu. "Low-Resource Named Entity Recognition via the Pre-Training Model." Symmetry 13, no. 5 (May 2, 2021): 786. http://dx.doi.org/10.3390/sym13050786.

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Named entity recognition (NER) is an important task in the processing of natural language, which needs to determine entity boundaries and classify them into pre-defined categories. For low-resource languages, most state-of-the-art systems require tens of thousands of annotated sentences to obtain high performance. However, there is minimal annotated data available about Uyghur and Hungarian (UH languages) NER tasks. There are also specificities in each task—differences in words and word order across languages make it a challenging problem. In this paper, we present an effective solution to providing a meaningful and easy-to-use feature extractor for named entity recognition tasks: fine-tuning the pre-trained language model. Therefore, we propose a fine-tuning method for a low-resource language model, which constructs a fine-tuning dataset through data augmentation; then the dataset of a high-resource language is added; and finally the cross-language pre-trained model is fine-tuned on this dataset. In addition, we propose an attention-based fine-tuning strategy that uses symmetry to better select relevant semantic and syntactic information from pre-trained language models and apply these symmetry features to name entity recognition tasks. We evaluated our approach on Uyghur and Hungarian datasets, which showed wonderful performance compared to some strong baselines. We close with an overview of the available resources for named entity recognition and some of the open research questions.

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22

Yidemucao, Dawa, Aniwar Tohuti, Qing Yu, and Maimaiti Roukeyanmu. "A Transformation Approach between the Similar Language Text." Advanced Materials Research 791-793 (September 2013): 1716–20. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1716.

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This paper first investigates the similarity level between the same family and closer languages (such as Altai family languages) and then examines a transformation between their entries and texts. Cosine similarity measure and dynamic programming (DP) algorithm are used to calculate the similarity and transformation between the source and target languages using a multilingual parallel data set and a function word dictionary. Test data set includes 7,854 paralleled sentences of Chinese, Uyghur, Kazakh and Mongolian various writing systems. Experimental results show that the similarity level of the languages from the same language branch is higher than that between different language branches. And a transformation test focused on the Mongolian language branch showed accuracy of 86.7% for NM to TM and 91.1% for NM to TODO.

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23

Abliz, Wayit, Maihemuti Maimaiti, Hao Wu, Jiamila Wushouer, Kahaerjiang Abiderexiti, Tuergen Yibulayin, and Aishan Wumaier. "Research on Uyghur Pattern Matching Based on Syllable Features." Information 11, no. 5 (May 2, 2020): 248. http://dx.doi.org/10.3390/info11050248.

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Pattern matching is widely used in various fields such as information retrieval, natural language processing (NLP), data mining and network security. In Uyghur (a typical agglutinative, low-resource language with complex morphology, spoken by the ethnic Uyghur group in Xinjiang, China), research on pattern matching is also ongoing. Due to the language characteristics, the pattern matching using characters and words as basic units has insufficient performance. There are two problems for pattern matching: (1) vowel weakening and (2) morphological changes caused by suffixes. In view of the above problems, this paper proposes a Boyer–Moore-U (BM-U) algorithm and a retrievable syllable coding format based on the syllable features of the Uyghur language and the improvement of the Boyer–Moore (BM) algorithm. This algorithm uses syllable features to perform pattern matching, which effectively solves the problem of weakening vowels, and it can better match words with stem shape changes. Finally, in the pattern matching experiments based on character-encoded text and syllable-encoded text for vowel-weakened words, the BM-U algorithm precision, recall, F1-measure and accuracy are improved by 4%, 55%, 33%, 25% and 10%, 52%, 38%, 38% compared to the BM algorithm.

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Tohti, Turdi, Jimmy Huang, Askar Hamdulla, and Xing Tan. "Text Filtering through Multi-Pattern Matching: A Case Study of Wu–Manber–Uy on the Language of Uyghur." Information 10, no. 8 (July 24, 2019): 246. http://dx.doi.org/10.3390/info10080246.

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Given its generality in applications and its high time-efficiency on big data-sets, in recent years, the technique of text filtering through pattern matching has been attracting increasing attention from the field of information retrieval and Natural language Processing (NLP) research communities at large. That being the case, however, it has yet to be seen how this technique and its algorithms, (e.g., Wu–Manber, which is also considered in this paper) can be applied and adopted properly and effectively to Uyghur, a low-resource language that is mostly spoken by the ethnic Uyghur group with a population of more than eleven-million in Xinjiang, China. We observe that technically, the challenge is mainly caused by two factors: (1) Vowel weakening and (2) mismatching in semantics between affixes and stems. Accordingly, in this paper, we propose Wu–Manber–Uy, a variant of an improvement to Wu–Manber, dedicated particularly for working on the Uyghur language. Wu–Manber–Uy implements a stem deformation-based pattern expansion strategy, specifically for reducing the mismatching of patterns caused by vowel weakening and spelling errors. A two-way strategy that applies invigilation and control on the change of lexical meaning of stems during word-building is also used in Wu–Manber–Uy. Extra consideration with respect to Word2vec and the dictionary are incorporated into the system for processing Uyghur. The experimental results we have obtained consistently demonstrate the high performance of Wu–Manber–Uy.

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Parhat, Sardar, Mijit Ablimit, and Askar Hamdulla. "A Robust Morpheme Sequence and Convolutional Neural Network-Based Uyghur and Kazakh Short Text Classification." Information 10, no. 12 (December 6, 2019): 387. http://dx.doi.org/10.3390/info10120387.

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In this paper, based on the multilingual morphological analyzer, we researched the similar low-resource languages, Uyghur and Kazakh, short text classification. Generally, the online linguistic resources of these languages are noisy. So a preprocessing is necessary and can significantly improve the accuracy. Uyghur and Kazakh are the languages with derivational morphology, in which words are coined by stems concatenated with suffixes. Usually, terms are used as the representation of text content while excluding functional parts as stop words in these languages. By extracting stems we can collect necessary terms and exclude stop words. Morpheme segmentation tool can split text into morphemes with 95% high reliability. After preparing both word- and morpheme-based training text corpora, we apply convolutional neural network (CNN) as a feature selection and text classification algorithm to perform text classification tasks. Experimental results show that the morpheme-based approach outperformed the word-based approach. Word embedding technique is frequently used in text representation both in the framework of neural networks and as a value expression, and can map language units into a sequential vector space based on context, and it is a natural way to extract and predict out-of-vocabulary (OOV) from context information. Multilingual morphological analysis has provided a convenient way for processing tasks of low resource languages like Uyghur and Kazakh.

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26

Long Yu, Shengwei Tian, and Jun Huang. "Emotion Identification of Uyghur Sentence Based on Language Feature." International Journal of Digital Content Technology and its Applications 5, no. 10 (October 31, 2011): 31–38. http://dx.doi.org/10.4156/jdcta.vol5.issue10.4.

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27

Imam, Seyyare, Hankiz Yilahun, Akbar Pattar, and Askar Hamdulla. "The Intonation Pattern of Imperative Sentences in Uyghur Language." International Journal of Multimedia and Ubiquitous Engineering 11, no. 5 (May 31, 2016): 221–30. http://dx.doi.org/10.14257/ijmue.2016.11.5.20.

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Abliz, Wayit, Hao Wu, Maihemuti Maimaiti, Jiamila Wushouer, Kahaerjiang Abiderexiti, Tuergen Yibulayin, and Aishan Wumaier. "A Syllable-Based Technique for Uyghur Text Compression." Information 11, no. 3 (March 23, 2020): 172. http://dx.doi.org/10.3390/info11030172.

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To improve utilization of text storage resources and efficiency of data transmission, we proposed two syllable-based Uyghur text compression coding schemes. First, according to the statistics of syllable coverage of the corpus text, we constructed a 12-bit and 16-bit syllable code tables and added commonly used symbols—such as punctuation marks and ASCII characters—to the code tables. To enable the coding scheme to process Uyghur texts mixed with other language symbols, we introduced a flag code in the compression process to distinguish the Unicode encodings that were not in the code table. The experiments showed that the 12-bit coding scheme had an average compression ratio of 0.3 on Uyghur text less than 4 KB in size and that the 16-bit coding scheme had an average compression ratio of 0.5 on text less than 2 KB in size. Our compression schemes outperformed GZip, BZip2, and the LZW algorithm on short text and could be effectively applied to the compression of Uyghur short text for storage and applications.

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Grose, Timothy. "Uyghur Language Textbooks: Competing Images of a Multi-Ethnic China." Asian Studies Review 36, no. 3 (August 23, 2012): 369–89. http://dx.doi.org/10.1080/10357823.2012.711809.

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Imam, Seyyare, Aynur Nurtay, Akbar Pattar, and Askar Hamdulla. "The Patterns of Vowels in Monosyllabic Words of Uyghur Language." International Journal of Database Theory and Application 9, no. 3 (March 31, 2016): 113–22. http://dx.doi.org/10.14257/ijdta.2016.9.3.13.

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31

Tusun, Alimujiang, and Henriëtte Hendriks. "Voluntary motion events in Uyghur: A typological perspective." Lingua 226 (July 2019): 69–88. http://dx.doi.org/10.1016/j.lingua.2019.05.003.

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32

ZULPIYE, Aman, Hamdulla ASKAR, and Tursu DILMURAT. "Acoustic analysis of prosodic features of trisyllabic words in Uyghur language." Journal of Computer Applications 29, no. 7 (July 30, 2009): 2032–34. http://dx.doi.org/10.3724/sp.j.1087.2009.02032.

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33

Tang, Wenfang, Yue Hu, and Shuai Jin. "Affirmative Inaction: Education, Language Proficiency, and Socioeconomic Attainment Among China's Uyghur Minority." Chinese Sociological Review 48, no. 4 (September 8, 2016): 346–66. http://dx.doi.org/10.1080/21620555.2016.1202753.

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GARAMTSEREN, BAYARJARGAL. "Re-Establishment of the Christian Church in Mongolia: The Mongolian Standard Version Translation by National Christians." Unio Cum Christo 2, no. 2 (October 1, 2016): 49. http://dx.doi.org/10.35285/ucc2.2.2016.art3.

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Abstract: This paper has two main parts: the history of Christianity in Mongolia and the history of Bible translation in the Mongolian language. The history of Christianity in Mongolia and among the Mongols, especially before and during the Mongol Empire, is largely understudied and unknown. I will attempt to show that four tribes, the Kerait, the Naiman, the Onguud, and the Uyghur, who were important parts of the Mongol Empire, had already become Christian, with their own church structures and tradition, by the thirteenth century. Giving the history of Christianity up until the present time, I brieﬂy outline the seven-hundred-year history of Bible translation into the Mongolian language. At the end, I describe the Mongolian Standard Version project, an ongoing activity of Bible translation from the original languages by national Christians.

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TUERDI, Tuoheti, Mushajiang WEINILA, and Aimudula AISIKAER. "Intelligent method for word grouping based on frequent pattern mining in Uyghur language." Journal of Computer Applications 32, no. 10 (May 23, 2013): 2920–22. http://dx.doi.org/10.3724/sp.j.1087.2012.02920.

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36

Yan, Chenggang, Hongtao Xie, Shun Liu, Jian Yin, Yongdong Zhang, and Qionghai Dai. "Effective Uyghur Language Text Detection in Complex Background Images for Traffic Prompt Identification." IEEE Transactions on Intelligent Transportation Systems 19, no. 1 (January 2018): 220–29. http://dx.doi.org/10.1109/tits.2017.2749977.

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37

Ido, Shinji. "Vowel alternation in disyllabic reduplicatives: an areal dimension." Eesti ja soome-ugri keeleteaduse ajakiri. Journal of Estonian and Finno-Ugric Linguistics 2, no. 1 (June 17, 2011): 185–94. http://dx.doi.org/10.12697/jeful.2011.2.1.12.

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This paper analyzes a variety of languages with regard to vowel alternation patterns in their disyllabic sound symbolic reduplicatives (DSRs). The analysis reveals that (1) a number of different languages have their preferred patterns of vowel alternation for DSRs (e.g. /I/-/ᴅ/ in ding-dong and tick-tock in English) and (2) the relative height of each vowel against the other in a DSRis a linguistic feature that is primarily areal. The languages surveyed in this paper include Bukharan Tajik, Chinese, English, German, Kazakh, Korean, Manchu, Mongolian, Persian, Qarakhanid Turkic, Tatar, Tatar in Xinjiang, Turkish, Tuvan, Uyghur, Uzbek, and Uzbek in Xinjiang.

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38

Han, Yawen, Peter I. De Costa, and Yaqiong Cui. "Exploring the language policy and planning/second language acquisition interface: ecological insights from an Uyghur youth in China." Language Policy 18, no. 1 (April 20, 2018): 65–86. http://dx.doi.org/10.1007/s10993-018-9463-9.

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39

Korosteleva, Kristina V. "SI 4904: Сonservation as a Base for New Discoveries." Written Monuments of the Orient 6, no. 2 (February 9, 2021): 114–19. http://dx.doi.org/10.17816/wmo49894.

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Book fragments in Old Uyghur language, that constitute the major part of the Serindia collection, currently undergo conservation and preservation procedures. The throughout conservation started in 2019 showed, that modern methods not only give new life to ancient texts, but also contribute to the academic research. The article sought to describe conservation procedures of the particular fragment SI 4904 from the Serindia collection, as well as subsequently made discoveries.

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Brose, Michael. "Uyghur Technologists of Writing and Literacy in Mongol China." T'oung Pao 91, no. 4 (2005): 396–435. http://dx.doi.org/10.1163/156853205774910106.

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41

Clothey, Rebecca A. "The Internet as a tool for informal education: a case of Uyghur language websites." Compare: A Journal of Comparative and International Education 47, no. 3 (March 10, 2017): 344–58. http://dx.doi.org/10.1080/03057925.2017.1281103.

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Pan, Yirong, Xiao Li, Yating Yang, and Rui Dong. "Multi-Source Neural Model for Machine Translation of Agglutinative Language." Future Internet 12, no. 6 (June 3, 2020): 96. http://dx.doi.org/10.3390/fi12060096.

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Benefitting from the rapid development of artificial intelligence (AI) and deep learning, the machine translation task based on neural networks has achieved impressive performance in many high-resource language pairs. However, the neural machine translation (NMT) models still struggle in the translation task on agglutinative languages with complex morphology and limited resources. Inspired by the finding that utilizing the source-side linguistic knowledge can further improve the NMT performance, we propose a multi-source neural model that employs two separate encoders to encode the source word sequence and the linguistic feature sequences. Compared with the standard NMT model, we utilize an additional encoder to incorporate the linguistic features of lemma, part-of-speech (POS) tag, and morphological tag by extending the input embedding layer of the encoder. Moreover, we use a serial combination method to integrate the conditional information from the encoders with the outputs of the decoder, which aims to enhance the neural model to learn a high-quality context representation of the source sentence. Experimental results show that our approach is effective for the agglutinative language translation, which achieves the highest improvements of +2.4 BLEU points on Turkish–English translation task and +0.6 BLEU points on Uyghur–Chinese translation task.

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43

Kozintcev, Mark A. "Turkic Poetic Passages Written in Uyghur Vertical Script from the Collection of the Institute of Oriental Manuscripts, RAS." Письменные памятники Востока 17, no. 3 (October 26, 2020): 89–98. http://dx.doi.org/10.17816/wmo46591.

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The article contains photos housed at the Photograph Collection of the Institute of Oriental Manuscripts RAS (folder Appendix No. 2, shelf mark ФВ-277/10) and their description. The photos feature short texts written in Uyghur vertical script on the margins of an unknown manuscript. The language of the manuscript itself is unknown, although some photos let us presume that it used the Arabic script. The texts on the photos are accompanied by their transcriptions in Cyrillic and Arabic letters made by W. Radloff. Examination of the verses has shown that they belong to several authors, including masters of medieval Turkic poetry.

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Guo, Xiaoyan Grace, and Mingyue Michelle Gu. "Identity construction through English language learning in intra-national migration: a study on Uyghur students in China." Journal of Ethnic and Migration Studies 42, no. 14 (July 11, 2016): 2430–47. http://dx.doi.org/10.1080/1369183x.2016.1205942.

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45

陈, 春荣. "A Comparative Analysis of Additional Elements in the Formation of Words Representing Persons in Modern Uyghur Language." Modern Linguistics 09, no. 03 (2021): 818–26. http://dx.doi.org/10.12677/ml.2021.93111.

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46

Chungmin Lee. "Factivity Alternation of Attitude ‘know’ in Korean, Mongolian, Uyghur, Manchu, Azeri, etc. and Content Clausal Nominals." Journal of Cognitive Science 20, no. 4 (December 2019): 451–503. http://dx.doi.org/10.17791/jcs.2019.20.4.451.

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47

Wu, Struys, and Lochtman. "Relationship Between Language Dominance and Stimulus-Stimulus or Stimulus-Response Inhibition in Uyghur-Chinese Bilinguals with an Investigation of Speed-Accuracy Trade-Offs." Behavioral Sciences 9, no. 4 (April 18, 2019): 41. http://dx.doi.org/10.3390/bs9040041.

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The effect of bilingualism on inhibition control is increasingly under ongoing exploration. The present study primarily investigated the effect of within bilingual factors (i.e., dominance types of Uyghur-Chinese bilinguals) on a Stimulus-Stimulus task (Flanker) and a Stimulus-Response task (Simon). We also compared the bilinguals' performance on each type of cognitive control task in respect to a possible trade-off between speed and accuracy. The findings showed no explicit differences on performance in response time or accuracy among balanced, L1-dominant and L2-dominant bilinguals but balanced bilinguals demonstrated a significant speed-accuracy trade-off in the overall context switching between non-conflict and conflict trials in both cognitive control tasks where monitoring process is highly demanded. Additionally, all bilinguals across all language dominance types showed a trade-off strategy in inhibition during a Stimulus-Stimulus conflict (flanker task). This evidence indicates that the differences of within bilinguals in cognitive control could lie in the monitoring process, while for all bilinguals, inhibition during a Stimulus-Stimulus conflict could be a major component in the mechanism of bilingual language processing.

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Mirkamal and Zieme. "Further Fragments of the Guanwuliangshoufo jing 癀無渱壽佛經 in Old Uyghur." Central Asiatic Journal 63, no. 1-2 (2020): 273. http://dx.doi.org/10.13173/centasiaj.63.1-2.0273.

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Cabras, Giulia. "Language Contact in Modern Uyghur. By Aminem Memtimin. Turcologica 108, Harrassowitz Verlag: Wiesbaden, Germany, 2016, 245p.; ISBN: 978-3-447-10631-3." Languages 3, no. 4 (November 2, 2018): 41. http://dx.doi.org/10.3390/languages3040041.

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Clothey, Rebecca A., Emmanuel F. Koku, Erfan Erkin, and Husenjan Emat. "A voice for the voiceless: online social activism in Uyghur language blogs and state control of the Internet in China." Information, Communication & Society 19, no. 6 (July 9, 2015): 858–74. http://dx.doi.org/10.1080/1369118x.2015.1061577.

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