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Register a new userSemi-Supervised and Unsupervised Sense Annotation via Translations
الشرح شبه الإشراف والإشراف غير المنسق عبر الترجمات
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لا يزال الاستحواذ على بيانات التدريب المتعدد اللغات يمثل تحديا في غزالة معنى الكلمة (WSD).لمعالجة هذه المشكلة، اقترحت النهج غير الخاضعة للكالة لإنشاء التعليقات التوضيحية بالمعنى تلقائيا لتدريب أنظمة WSD الخاضعة للإشراف.نقدم ثلاث طرق جديدة لإنشاء كوربورا المعشوفة بالشعور التي تستفيد الترجمات، وثبات الموازية، والموارد المعجمية، وكذلك تضمينات السياق والتركيب.تطبق أسلوبنا شبه الإشراف ترجمة الجهاز لنقل التعليقات التوضيحية القائمة إلى لغات أخرى.طرقنا اثنين من الأساليب غير الخاضعة لعمليات إعادة صياغة الشرح بالمعنى الناتج عن نظام WSD القائم على المعرفة عبر الترجمات المعجمية في كوربوس متوازي.نحصل على نتائج حديثة على معايير WSD القياسية.
Acquisition of multilingual training data continues to be a challenge in word sense disambiguation (WSD). To address this problem, unsupervised approaches have been proposed to automatically generate sense annotations for training supervised WSD systems. We present three new methods for creating sense-annotated corpora which leverage translations, parallel bitexts, lexical resources, as well as contextual and synset embeddings. Our semi-supervised method applies machine translation to transfer existing sense annotations to other languages. Our two unsupervised methods refine sense annotations produced by a knowledge-based WSD system via lexical translations in a parallel corpus. We obtain state-of-the-art results on standard WSD benchmarks.
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