Machine Learning for Holistic Evaluation of Scientific Essays
Machine Learning for Holistic Evaluation of Scientific Essays
复制标题
用于科学论文整体评估的机器学习
DOI：
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发表时间：
2015
期刊：
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通讯作者：
Dylan Blaum
中科院分区：
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作者：
S. Hughes;P. Wiemer;M. Britt;Patricia S. Wallace;Dylan Blaum
特别是在美国，人们越来越强调科学在教育中的重要性。为了更好地理解一个科学主题，学生需要从多个来源收集信息，并确定所涉及的主要因果因素。我们描述了一种方法，使用一种新的，两阶段的机器学习方法来检测因果关系，自动推断的质量和完整性的因果推理在两个独立的科学主题的文章。对于每个核心概念，我们最初训练了一个基于窗口的标记模型来预测哪些单词属于该概念。使用来自第一组模型的预测，我们然后在句子中存在的所有预测单词标签上训练第二个堆叠模型，以预测文章概念之间的推断。结果表明，我们可以使用这样一个系统，以提供明确的反馈给学生，以提高推理和论文写作技能。
In the US in particular, there is an increasing emphasis on the importance of science in education. To better understand a scientific topic, students need to compile information from multiple sources and determine the principal causal factors involved. We describe an approach for automatically inferring the quality and completeness of causal reasoning in essays on two separate scientific topics using a novel, two-phase machine learning approach for detecting causal relations. For each core essay concept, we initially trained a window-based tagging model to predict which individual words belonged to that concept. Using the predictions from this first set of models, we then trained a second stacked model on all the predicted word tags present in a sentence to predict inferences between essay concepts. The results indicate we could use such a system to provide explicit feedback to students to improve reasoning and essay writing skills.