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Register a new userNeural Borrowing Detection with Monolingual Lexical Models
اكتشاف الاقتراض العصبي مع نماذج معجمية أحادية
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تحديد القروض المعجمية، ونقل الكلمات بين اللغات، هي ممارسة أساسية لللغويات التاريخية وأداة حيوية في تحليل اتصال اللغة والأحداث الثقافية بشكل عام.نسعى لتحسين الأدوات للكشف التلقائي للقروض المعجمية، مع التركيز هنا على الكشف عن الكلمات المقترضة من نصوص الكلمات أحادية الأحادية.بدءا من نموذج اللغة المعجمية العصبية المتكررة ونهج انتروبيات المنافسة، فإننا ندمج نموذجا أكثر قائما على المحولات القائمة على المحولات.من هناك، نقوم بتجربة العديد من النماذج والنهج المختلفة بما في ذلك نموذج الجهات المانحة المعجمية مع قائمة الكلمات المعززة.يقلل نموذج المحول وقت التنفيذ ويحسن الحد الأدنى للكشف عن الاقتراض.نموذج المانحين المعزز يظهر بعض الوعد.هناك حاجة إلى تغيير موضوعي في النهج أو النموذج لإجراء مكاسب كبيرة في تحديد القروض المعجمية.
Identification of lexical borrowings, transfer of words between languages, is an essential practice of historical linguistics and a vital tool in analysis of language contact and cultural events in general. We seek to improve tools for automatic detection of lexical borrowings, focusing here on detecting borrowed words from monolingual wordlists. Starting with a recurrent neural lexical language model and competing entropies approach, we incorporate a more current Transformer based lexical model. From there we experiment with several different models and approaches including a lexical donor model with augmented wordlist. The Transformer model reduces execution time and minimally improves borrowing detection. The augmented donor model shows some promise. A substantive change in approach or model is needed to make significant gains in identification of lexical borrowings.
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