Algorithm for Predicting Mathematical Formulae from Linear Strings for Mathematical Inputs
Algorithm for Predicting Mathematical Formulae from Linear Strings for Mathematical Inputs
复制标题
从数学输入的线性字符串预测数学公式的算法
DOI：
10.1007/978-3-319-56932-1_9
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Tetsuo Fukui
中科院分区：
文献类型：
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作者：
Yasuyuki Nakamura;Kentaro Yoshitomi;Mitsuru Kawazoe;Tetsuo Fukui;Shizuka Shirai;Takahiro Nakahara;Katsuya Kato;Tetsuya Taniguchi;Tetsuo Fukui and Shizuka Shirai;Tetsuo Fukui
最近，计算机辅助评估（CAA）系统已被用于数学教育，一些CAA系统能够使用数学表达式评估学习者的答案。然而，数学教育系统的标准输入法对初学者来说很麻烦。在2011年，我们提出了一种新的数学输入法，允许用户在所见即所得中通过对口语化线性字符串的数学表达式进行交互转换来输入数学表达式。在本研究中，我们提出了一种预测算法，通过类似于自然语言处理的结构化感知器，使用机器学习来确定分数参数，从而提高该转换过程的输入效率。在我们的实验评估中，使用包含700个公式的训练数据集，通过稳定分数参数学习，预测准确率为96.2%，排名前十；这种精度对于数学输入接口系统是足够的。
Recently, computer-aided assessment (CAA) systems have been used for mathematics education, with some CAA systems capable of assessing learners’ answers using mathematical expressions. However, the standard input method for mathematics education systems is cumbersome for novice learners. In 2011, we proposed a new mathematical input method that allowed users to input mathematical expressions through an interactive conversion of mathematical expressions from colloquial-style linear strings in WYSIWYG. In this study, we propose a predictive algorithm to improve the input efficiency of this conversion process by using machine learning to determine the score parameters with a structured perceptron similar to natural language processing. In our experimental evaluation, with a training dataset comprising 700 formulae, the prediction accuracy was 96.2% for the top ten ranking by stable score parameter learning; this accuracy is sufficient for a mathematical input interface system.