خلاصه‌سازی تک سندی متون فارسی به کمک یادگیری عمیق ماشینی
  •   آرشیو : نسخه زمستان 1397
  •   موضوع : هوش مصنوعی
  • نویسنده/گان : علیرضا محسنی ، ونوس مرضی، امیرحسین جدیدی نژاد
  •   چکیده :
    در سال‌‌‌های اخیر نرخِ رشدِ اطلاعات، بسیار فزاینده بوده و نیاز به سیستم‌های خلاصه‌ساز متن احساس می‌شود. خلاصه‌سازي خودکار سند به معنی توليد يک نسخه مختصرتر از سند اصلي با حفظ نکات کلیدی به وسیله کامپیوتر می‌باشد. روش‌های مختلفی جهت خلاصه‌سازی متون فارسی تاکنون به کار گرفته شده است که از روش‌های استخراجی استفاده می‌نمایند که در این روش جملات مهم براساس الگوریتم‌هایی استخراج و به یکدیگر متصل می‌گردند همچنین در آخرین روش‌ها برای بهبود نتایج از یادگیری ماشینی استفاده شده ، لذا تاکنون در زمینه‌ی خلاصه‌سازی به کمک یادگیری عمیق ماشینی کمتر کاری انجام شده است. یادگیری عمیق ماشینی از شبکه‌های عصبی عمیق مصنوعی استفاده می‌نماید که نتایج بسیار خوبی در مقایسه با روش‌های مرسوم از خود نشان داده است. ما جهت خلاصه‌سازی متون فارسی از شبکه‌های عمیق انکودر-دکودر استفاده نمودیم که این دسته از شبکه‌ها قابلیت بالایی در استخراج ویژگی دارا می‌باشند. ما جهت ایجاد زیرساخت‌های نرم افزاری لازم از آخرین فناوری شرکت گوگل یعنی تنسورفلو استفاده نمودیم همچنین یکی از دستاوردهای این تحقیق توسعه یک دیتاست فارسی سازگار با مدل نهایی ما بر پایه مجموعه همشهری می باشد و بر اساس آن شبکه خود را آموزش دادیم. واژگان کليدي: خلاصه‌سازی خودکار متن، تسورفلو، یادگیری عمیق، شبکه عصبی، انکودر-دکودر، شبکه عصبی بازگشتی. مسأله جدول زمانبندی دروس دانشگاهی، یک فرآیند زمانبندی دروس دانشگاهی برای یک نیم سال تحصیلی توسط دانشکده¬های یک دانشگاه می¬باشد که ذاتا مسأله¬ای است در گروه مسائل سخت از نوع دسته¬ی مسائل NP-Complete. این مسأله رویدادها (استادان/دانشجویان/دروس) را در منابع (برش¬های زمانی/ کلاس¬های درسی) زمانبندی و تخصیص می¬دهد که این فرآیند تخصیص دارای دو قید حساس می¬باشد که این قیود سخت و نرم می¬باشند. هدف مسأله بهبود قیود نرم می¬باشد. در این مقاله، رهیافت¬های مورد بررسی برای زمانبندی استادان (مشترک مابین) دانشکده¬(ها) شامل، الگوریتم¬های خوشه¬بندی (K- میانگین، C- میانگین فازی و قیفی)، مقایسه¬ی تصمیم گیری چند معیاره¬ی فازی، ترکیبی (جستجوی محلی/ ژنتیک) و ترکیب الگوریتم¬های خوشه¬¬بندی با مقایسه¬ی تصمیم گیری چند معیاره¬ی فازی با ترکیبی می¬باشد. البته بهینگی و مقایسه¬ی کارآیی عملکرد الگوریتم¬های به کار گرفته شده در این مقاله به طور کامل مورد تحلیل و بررسی دقیق قرار گرفته است.
  •   کلید واژه : جدول زمانبندی دروس دانشگاهی، الگوریتم¬های خوشه¬بندی، الگوریتم مقایسه¬ی تصمیم گیری چند معیاره¬ی فازی و الگوریتم ترکیبی.
  •   Title : A New Approach to Solve University Course Timetabling Problem
  •   Abstract :
    University Course Time-Tabling Problem is a process of scheduling university courses for one semester by the faculties of a university, which is inherently NP-Complete problem. The main technique in the presented approach is focused on developing and making the process of timetabling common lecturers among different departments of a university scalable. This problem schedules and allocates events (lecturers/ students/ courses) to resources (time slots/ classrooms), which has two sensitive constraints including hard and soft constraints. The goal is to improve soft constraints. In this paper, the studied approaches include clustering algorithms (K-means, fuzzy C-means, and funnel), fuzzy multi-criteria decision making comparison, hybrid (local search/ genetic) and combination of clustering algorithms with fuzzy multi-criteria decision making comparison. For this, the optimization and performance comparisons of the algorithms used in this paper are thoroughly analyzed. Paper’s aims: 1) descending satisfaction of preferences and soft constraints of common lecturers among departments, 2) minimizing the loss of extra resources of each faculty. An applied dataset is based on meeting the requirements of scheduling in real world, among various departments of Islamic Azad University, Ahar Branch and the success of the results would be in respect of satisfying uniform distribution and allocation of common lecturers on extra resources among different departments.
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