面向慕课视频的关键信息检索系统设计

Key Information Retrieval System for MOOC Videos

随着互联网技术的迅猛发展,慕课等在线教育平台日益受到广泛关注。慕课作为一种创新的教育形式,有效突破了传统教育模式的地域界限,实现了优质教育资源的全球共享。通过慕课,学习者能够根据个人兴趣自主选择课程,灵活安排学习时间与进度,且能便利地进行重复学习。然而,当前慕课平台在针对授课视频中的特定知识点进行时间定位时,仍存在很大挑战,导致用户在学习关键核心知识点时需频繁拖动视频进度以寻找相应视频片段。针对这一现状,提出了一种基于多重二分匹配的注意力机制模型的慕课视频知识抽取算法。算法框架的主体部分包括字幕文本识别与生成、字幕文本分段提取、知识点抽取模型,以及知识点检索模块。实验结果表明,相对于当前的知识点抽取模型,所提模型在Inspec,NUS,Krapivin,SemEval,KP20k等多个数据集上,在部分关键指标上达到了当前的最优表现,充分证明了本系统在实际应用中的潜力和价值。

Thanks to the rapid advancement of Internet technology,online education platforms,particularly massive open online courses(MOOCs),have increasingly captured public attention.MOOCs represent a revolutionary educational approach,effectively eliminating the geographical boundaries inherent in traditional education models and fostering the worldwide dissemination of elite educational resources.These courses empower learners to cherry-pick courses based on their unique interests,create flexible study schedules,monitor their progress,and revisit materials as needed.Despite their versatility,current MOOC platforms still struggle to pinpoint precise knowledge nuggets within lecture videos.This often leads learners to constantly scrub through the video timeline,searching for relevant segments,thereby disrupting the learning continuum.In view of this situation,we introduce a MOOC video knowledge extraction algorithm,leveraging a multi-level binary matching attention mechanism model.This algorithmic framework integrates subtitle text recognition and generation,subtitle segment extraction,a knowledge point extraction model,and a retrieval module.Experimental results show that,compared with the current knowledge point extraction model,the method of this system has achieved the optimal performance on some key indicators on multiple datasets such as Inspec,NUS,Krapivin,SemEval,KP20k,which fully proves the potential and value of this system in practical applications.