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Register a new userScalable and Interpretable Semantic Change Detection
القابل للتغيير والتفسير القابل للتفسير
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ظهرت العديد من الطرق المستندة إلى العنقودية للكشف عن التغير الدلالي بموظفي السياق مؤخرا.إنهم يتيحون تحليلا غرامة لاستخدام كلمة التغيير عن طريق تجميع المدينات في مجموعات تعكس استخدامات الكلمة المختلفة.ومع ذلك، فإن هذه الطرق غير مستقرة من حيث استهلاك الذاكرة ووقت الحساب.لذلك، فإنها تتطلب مجموعة محدودة من الكلمات المستهدفة التي سيتم اختيارها مسبقا.هذا يحد بشكل كبير من قابلية استخدام هذه الأساليب في مهام الاستكشافية المفتوحة، حيث يمكن اعتبار كل كلمة من المفردات هدف محتمل.نقترح طريقة قابلة للتطوير الجديدة للكشف عن تغيير الكلمات التي توفر مكاسب كبيرة في وقت المعالجة وفورات كبيرة في الذاكرة مع تقدم نفس التفسير وأداء أفضل من الأساليب غير القابلة للتحصيل.نوضح إمكانية تطبيق الأسلوب المقترح من خلال تحليل جثة كبيرة من مقالات إخبارية حول Covid-19.
Several cluster-based methods for semantic change detection with contextual embeddings emerged recently. They allow a fine-grained analysis of word use change by aggregating embeddings into clusters that reflect the different usages of the word. However, these methods are unscalable in terms of memory consumption and computation time. Therefore, they require a limited set of target words to be picked in advance. This drastically limits the usability of these methods in open exploratory tasks, where each word from the vocabulary can be considered as a potential target. We propose a novel scalable method for word usage-change detection that offers large gains in processing time and significant memory savings while offering the same interpretability and better performance than unscalable methods. We demonstrate the applicability of the proposed method by analysing a large corpus of news articles about COVID-19.
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