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Register a new userCharacter-based Thai Word Segmentation with Multiple Attentions
تجزئة الكلمات التايلاندية القائمة على الشخصية مع انتباه متعددة
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تم تطبيق نماذج تجزئة الكلمات القائمة على الأحرف على نطاق واسع على اللغات الشاقة، بما في ذلك التايلاندية، بسبب أدائها العالي.هذه النماذج تقدر حدود الكلمات من تسلسل الأحرف.ومع ذلك، فإن وحدة الأحرف في تسلسل ليس لها معنى أساسي، مقارنة بكل وحدات الكتلة الكلمة والكلمة الفرعية.نقترح نموذج تجزئة الكلمات التايلاندية يستخدم أنواعا مختلفة من المعلومات، بما في ذلك الكلمات والكلمات الفرعية ومجموعات الأحرف، من تسلسل الأحرف.ينطبق نموذجنا على انتباه متعددة لتحسين استنتاجات تجزئة من خلال تقدير العلاقات الكبيرة بين الشخصيات وأنواع الوحدات المختلفة.تشير النتائج التجريبية إلى أن نموذجنا يمكن أن يتفوق على نماذج تجزئة الكلمات التايلاندية الأخرى.
Character-based word-segmentation models have been extensively applied to agglutinative languages, including Thai, due to their high performance. These models estimate word boundaries from a character sequence. However, a character unit in sequences has no essential meaning, compared with word, subword, and character cluster units. We propose a Thai word-segmentation model that uses various types of information, including words, subwords, and character clusters, from a character sequence. Our model applies multiple attentions to refine segmentation inferences by estimating the significant relationships among characters and various unit types. The experimental results indicate that our model can outperform other state-of-the-art Thai word-segmentation models.
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