أدى استخدام الأساليب التلقائية لدراسة التغيير الدلالي المعجمي (LSC) إلى إنشاء معايير التقييم.ومع ذلك، يتم ربط مجموعات البيانات القياسية بالجوربوس المستخدمة لإنشائها استجواب موثوقيتها وكذلك متانة الأساليب التلقائية.تحقق هذه المساهمة في هذه الجوانب التي توضح تأثير الأبعاد الاجتماعية والثقافية غير المتوقعة.نحن نحدد أيضا مجموعة من المشكلات الإضافية (جودة التعرف الضوئي على الحروف بالإنترنت، والكيانات المسماة) التي تؤثر على أداء الأساليب التلقائية، خاصة عند استخدامها لاكتشاف LSC.
The use of automatic methods for the study of lexical semantic change (LSC) has led to the creation of evaluation benchmarks. Benchmark datasets, however, are intimately tied to the corpus used for their creation questioning their reliability as well as the robustness of automatic methods. This contribution investigates these aspects showing the impact of unforeseen social and cultural dimensions. We also identify a set of additional issues (OCR quality, named entities) that impact the performance of the automatic methods, especially when used to discover LSC.
References used
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