تشخيص موضع به زبان فارسی مبتنی بر طبقه بندهای چندگانه
محورهای موضوعی : فناوری اطلاعات و ارتباطات
مژگان فرهودی
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عباس طلوعی اشلقی
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1 - عضو هیات علمی
2 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات
کلید واژه: تشخیص موضع, طبقه بند چندگانه, يادگيری ماشين, يادگيری عميق, يادگيری انتقالی.,
چکیده مقاله :
تشخيص موضع (که با عناوبن طبقه‌بندي موضع، تحليل موضع يا پيش‌بيني موضع نيز شناخته شده است) يک موضوع تحقيقاتي اخير است که به يک پارادايم نوظهور تبديل شده است. هدف از تشخيص موضع، شناسايي موضع نويسنده نسبت به يک موضوع يا ادعاي خاص بوده که به جزء کليدي کاربردهايي مانند تشخيص اخبار جعلي، اعتبارسنجي ادعا يا جستجوي استدلال تبديل شده است. در اين مقاله از سه رويکرد يادگيري ماشين، يادگيري عميق و يادگيري انتقالي براي تشخيص موضع فارسي استفاده شده و سپس با بکارگيری طبقهبندهای چندگانه، مدلی برای اخذ تصميم نهايي در مورد نتايج خروجي پيشنهاد گرديده است. برای اين منظور از روش اکثريت آرا مبتنی بر صحت طبقهبندهای انفرادی براي ترکيب نتايج آنها استفاده گرديد. نتايج آزمايش‌ها نشان داد که عملکرد مدل پيشنهادي نسبت به عملکرد طبقهبندهای انفرادی پيشرفت مناسبی داشته است.
Stance detection (also known as stance classification, stance prediction, and stance analysis) is a recent research topic that has become an emerging paradigm of the importance of opinion-mining. The purpose of stance detection is to identify the author's viewpoint toward a specific target, which has become a key component of applications such as fake news detection, claim validation, argument search, etc. In this paper, we applied three approaches including machine learning, deep learning and transfer learning for Persian stance detection. Then we proposed a framework of multi-classifier fusion for getting final decision on output results. We used a weighted majority voting method based on the accuracy of the classifiers to combine their results. The experimental results showed the performance of the proposed multi-classifier fusion method is better than individual classifiers.
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