论文标题
在煤矿中找到黑猫 - 从复杂分配
Finding Black Cat in a Coal Cellar -- Keyphrase Extraction & Keyphrase-Rubric Relationship Classification from Complex Assignments
论文作者
论文摘要
内容和开放性问题的多样性是在线研究生课程中复杂作业中固有的。这些程序的自然规模在同伴和专家反馈中构成了各种挑战，包括流氓评论。虽然识别相关内容并将其与预定义的标题相关联可以简化和改善分级过程，但迄今为止的研究仍处于新生的阶段。因此，在本文中，我们旨在量化监督和无监督的方法的有效性，用于提取键形和通用/特定的键形 - 鲁言关系提取的任务。通过这项研究，我们发现（i）无监督的多阶列克为钥匙酶提取（II）监督的SVM分类器带来了最佳结果，具有BERT功能，可为通用和特定的钥匙素 - 鲁言关系分类提供最佳性能。我们最终对对未来这些任务感兴趣的人进行了全面的分析，并获得了有用的观察结果。源代码在\ url {https://github.com/manikandan-ravikiran/cs6460-proj}中发布。
Diversity in content and open-ended questions are inherent in complex assignments across online graduate programs. The natural scale of these programs poses a variety of challenges across both peer and expert feedback including rogue reviews. While the identification of relevant content and associating it to predefined rubrics would simplify and improve the grading process, the research to date is still in a nascent stage. As such in this paper we aim to quantify the effectiveness of supervised and unsupervised approaches for the task for keyphrase extraction and generic/specific keyphrase-rubric relationship extraction. Through this study, we find that (i) unsupervised MultiPartiteRank produces the best result for keyphrase extraction (ii) supervised SVM classifier with BERT features that offer the best performance for both generic and specific keyphrase-rubric relationship classification. We finally present a comprehensive analysis and derive useful observations for those interested in these tasks for the future. The source code is released in \url{https://github.com/manikandan-ravikiran/cs6460-proj}.
