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Automatically solving two-variable linear algebraic word problems using text mining
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DOI：10.1111/exsy.12358.png)
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摘要
En 中文
The teaching and learning of algebraic word problems is a basic component of elementary education. Recently, to facilitate its learning, a few approaches for automatically solving algebraic and arithmetic word problems have been proposed. These systems generally use either natural language processing (NLP) or a combination of NLP and machine learning. However, they have low accuracy due to their large feature sets, extracted using limited preprocessing techniques. In this research work, we propose a template-based approach that was developed by following a two-step process. In the first step, we predict an equation template from a training dataset using NLP and a classification mechanism. The next step is to instantiate the predicted template with nouns and numbers through reasoning. To validate the proposed methodology, a prototype system was implemented. We then compared the proposed system with the existing systems using their respective datasets and the proposed dataset. The experimental results show improvement in accuracy, with an average precision of 80.6% and average recall of 83.5%.
Keyword:
application programme interface (API)
dependency parser
JEP (java expression parser)
machine learning (ML)
named entity recognition (NER)
natural language processing (NLP)
part of speech (POS)
random forest (RF)
term frequency (TF)
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