Automatic descriptive answer evaluator using machine learning.

Automating the evaluation of descriptive answers would be beneficial for academic institutions to

efficiently manage the online exam results of their students. Our project involves designing an

algorithm to automatically evaluate descriptive answers consisting of multiple sentences. Our

approach involves representing the student's answer and comparing it with pre-defined answers

created by the staff. To evaluate the answer, we use a pattern-matching algorithm and various

modules to achieve efficient evaluation without manual labor. This pattern can be used by many

organizations to reduce manpower and save time. Natural Language Processing (NLP) aims to

interpret human language in a meaningful way and typically involves machine learning techniques.

Evaluating the objective function involves assessing candidate solutions against a portion of the

training dataset, usually measured by an error score or loss. While the objective function is easy to

define, evaluating it can be costly.

对简答题进行自动化评阅，可助力学术机构高效管理学生在线考试成绩。本项目旨在设计一款算法，用于自动评阅由多个句子组成的简答题作答内容。本方案通过将学生作答内容与教职工预先设定的参考答案进行表征对比，实现自动评阅。评阅环节采用模式匹配算法与多模块协同方案，可在无需人工干预的前提下实现高效评阅。该方案可被众多机构采用，以缩减人力成本并节约时间。自然语言处理（Natural Language Processing，NLP）旨在以语义化方式理解人类语言，通常会结合机器学习技术开展相关工作。目标函数的评估环节，需基于部分训练数据集对候选解决方案进行性能校验，通常以误差得分或损失值作为衡量指标。尽管目标函数的定义较为简单，但其评估过程往往会产生较高的计算成本。