دوره 21، شماره 3 - ( 10-1403 )
جلد 21 شماره 3 صفحات 110-97 |
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Naghavi M, Hassani Ahangar M R, Amiri Jezeh A. Identify the Named Entities Using Deep Learning and Reinforcement Approach. JSDP 2024; 21 (3) : 5
URL: http://jsdp.rcisp.ac.ir/article-1-1232-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-1232-fa.html
نقوی مهدی، حسنی‌آهنگر محمدرضا، امیری جزه علی. شناسایی موجودیت‌های اسمی با استفاده از یادگیری عمیق و رویکرد تقویتی. پردازش علائم و داده‌ها. 1403; 21 (3) :97-110
استادیار دانشکده و پژوهشکده رایانه، شبکه و ارتباطات، دانشگاه جامع امام حسین (ع)، تهران، ایران
چکیده: (712 مشاهده)
مطالعات اخیر نشان می‌دهد که شناسایی موجودیت‌های اسمی به یکی از موضوعات پرطرف‌دار در حوزه پردازش زبان طبیعی تبدیل شده‌است؛ این افزایش توجه بیشتر ناشی از رشد سریع روش‌های نوین در هوش مصنوعی، به‌ویژه شبکه‌های عصبی است. ظهور شبکه‌های عصبی عمیق به‌دلیل توانایی‌های برجسته آن‌ها در حل مسائل پیچیده پردازش زبان طبیعی، فرصت‌هایی نوین برای بهبود عملکرد سامانه‌های شناسایی موجودیت‌های اسمی فراهم کرده‌است؛ یکی از عوامل کلیدی در افزایش دقت این سامانه‌ها، بهره‌گیری از ویژگی‌های پیشرفته‌ای است که تا پیش از ظهور یادگیری عمیق قابل دسترسی نبودند، از جمله استفاده از مدل‌های زبانی که توانایی درک معنای متن‌های بزرگ را دارند. در این مقاله، یک روش پیشنهادی مبتنی بر مدل زبانی معنایی در زبان فارسی و شبکه عصبی عمیق طراحی شده‌است که با استفاده از منطق تکرار، مشکل وابستگی به داده‌های زیاد را کاهش می‌دهد. این روش روی مجموعه‌داده CoNLL 2003 انگلیسی و دو مجموعه‌داده فارسی آرمان و پیما ارزیابی شده‌است؛ نتایج ارزیابی به ترتیب مقادیر 95.3، 96.32 و 94.72 را برای معیار F نشان داده که حاکی از بهبود عملکرد نسبت به روش‌های پیشین است.
شماره‌ی مقاله: 5
نوع مطالعه: پژوهشي |
موضوع مقاله:
مقالات پردازش متن
دریافت: 1400/2/25 | پذیرش: 1402/12/6 | انتشار: 1403/10/28 | انتشار الکترونیک: 1403/10/28
دریافت: 1400/2/25 | پذیرش: 1402/12/6 | انتشار: 1403/10/28 | انتشار الکترونیک: 1403/10/28
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