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الآن، إنه شخصي: الحاجة إلى غموض معنى كلمة الشخصية
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يميل مؤلفو النص إلى استخدام إحساس واحد في الغالب ل Lemma التي يمكن أن تختلف بين المؤلفين المختلفين.قد لا يتم التقاط هذا بنموذج Disambiguation Sense (WSD) المعذر (WSD) الذي تم تدريبه على المؤلفين المتعددين.يجد عملنا أن الحواس الأولى في WordNet، والحواس السائدة من نوع DataSet الخاص بنا، والاستحواذ السائدة للمؤلف يمكن أن تكون مختلفة، وبالتالي، يمكن أن تؤدي النماذج المؤلف للأذرع بشكل جيد على مجموعة البيانات بأكملها، ولكن بشكل سيء على المؤلفين الأفراد.في هذا العمل، نستكشف أساليب تخصيص نماذج WSD عن طريق خياطة النماذج الموجودة من أحدث النماذج نحو الفرد من خلال استغلال توزيعات معنى المؤلف.نقترح مجموعة بيانات WSD الجديدة وإظهار أن تخصيص نظام WSD مع معرفة توزيعات معنى المؤلف أو الحواس السائدة يمكن أن تزيد بشكل كبير أدائها.
Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors. Our work finds that WordNet's first senses, the predominant senses of our dataset's genre, and the predominant senses of an author can all be different and therefore, author-agnostic models could perform well over the entire dataset, but poorly on individual authors. In this work, we explore methods for personalizing WSD models by tailoring existing state-of-the-art models toward an individual by exploiting the author's sense distributions. We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author's sense distributions or predominant senses can greatly increase its performance.
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