Natural Language Processing (NLP) aims to analyze the text via techniques in
the computer science field. It serves the applications in healthcare, commerce,
and education domains. Particularly, NLP has been applied to the education
domain to help teaching and learning. In this survey, we review recent advances
in NLP with a focus on solving problems related to the education domain. In
detail, we begin with introducing the relevant background. Then, we present the
taxonomy of NLP in the education domain. Next, we illustrate the task
definition, challenges, and corresponding techniques based on the above
taxonomy. After that, we showcase some off-the-shelf demonstrations in this
domain and conclude with future directions.

自然语言处理（NLP）旨在通过计算机科学领域的技术来分析文本，适用于医疗、商业和教育领域的应用。本文回顾了近期在教育领域解决问题方面的 NLP 的最新进展，介绍相关背景，提出 NLP 在教育领域的分类体系，并基于该体系讨论任务定义、挑战以及相应技术。随后展示了该领域中的一些现有演示，并给出未来方向的结论。

教育自然语言处理综述：分类、系统回顾与未来趋势

Survey of Natural Language Processing for Education: Taxonomy,  Systematic Review, and Future Trends

Understanding learning materials (e.g. test questions) is a crucial issue in
online learning systems, which can promote many applications in education
domain. Unfortunately, many supervised approaches suffer from the problem of
scarce human labeled data, whereas abundant unlabeled resources are highly
underutilized. To alleviate this problem, an effective solution is to use
pre-trained representations for question understanding. However, existing
pre-training methods in NLP area are infeasible to learn test question
representations due to several domain-specific characteristics in education.
First, questions usually comprise of heterogeneous data including content text,
images and side information. Second, there exists both basic linguistic
information as well as domain logic and knowledge. To this end, in this paper,
we propose a novel pre-training method, namely QuesNet, for comprehensively
learning question representations. Specifically, we first design a unified
framework to aggregate question information with its heterogeneous inputs into
a comprehensive vector. Then we propose a two-level hierarchical pre-training
algorithm to learn better understanding of test questions in an unsupervised
way. Here, a novel holed language model objective is developed to extract
low-level linguistic features, and a domain-oriented objective is proposed to
learn high-level logic and knowledge. Moreover, we show that QuesNet has good
capability of being fine-tuned in many question-based tasks. We conduct
extensive experiments on large-scale real-world question data, where the
experimental results clearly demonstrate the effectiveness of QuesNet for
question understanding as well as its superior applicability.

提出了一种面向教育领域的预训练方法 QuesNet，利用统一的框架整合含有异构输入的问题信息并设计了两种级别的预训练算法来学习基础语言特征、领域逻辑和知识，证明了其在问题理解和应用方面的有效性和优越性。