嘈杂维吾尔文规范化

NUT@EMNLP Pub Date : 2017-09-01 DOI:10.18653/v1/W17-4412
Osman Tursun, Ruken Cakici
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引用次数: 13

摘要

维吾尔语是中国第二大、使用最活跃的社交媒体语言。然而,在社交媒体上出现的维吾尔语文本中,有一部分是用拉丁字母非系统地书写的,而且规模还在不断扩大。这种格式的维吾尔语文本即使对母语为维吾尔语的人来说也是难以理解和模棱两可的。此外,这种形式的维吾尔语文本在与维吾尔语相关的NLP任务中缺乏任何进步的潜力。恢复和防止用非系统拉丁字母书写的维吾尔文本的噪声,对于保护维吾尔语和提高维吾尔语自然语言处理任务的准确性至关重要。为此,我们提出并比较了噪声信道模型和神经编码器-解码器模型作为归一化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noisy Uyghur Text Normalization
Uyghur is the second largest and most actively used social media language in China. However, a non-negligible part of Uyghur text appearing in social media is unsystematically written with the Latin alphabet, and it continues to increase in size. Uyghur text in this format is incomprehensible and ambiguous even to native Uyghur speakers. In addition, Uyghur texts in this form lack the potential for any kind of advancement for the NLP tasks related to the Uyghur language. Restoring and preventing noisy Uyghur text written with unsystematic Latin alphabets will be essential to the protection of Uyghur language and improving the accuracy of Uyghur NLP tasks. To this purpose, in this work we propose and compare the noisy channel model and the neural encoder-decoder model as normalizing methods.
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