Abstract
Natural language processing (NLP) is generally done on large data. Due to limited data word problem solving is challenging using NLP. There are some approaches proposed which could solve basic arithmetic problems like addition/subtraction. Knowledge representation is the main task to be done by NLP. Each kind of problem has its own approach. In this paper three types of mathematical word problems have been solved. Two of them are aptitude problems while the other two are reasoning problems. The spacy library has been used for effective use of Named Entity Recognition (NER) and word vectors. Stepwise solution has been generated instead of just answers which helps in improving understanding. The quite generic rather intuitive approach can be extended to solve some other kind of aptitude problems.
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Ughade, S., Kumbhar, S. (2020). Mathematical Word Problem Solving Using Natural Language Processing. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1077. Springer, Singapore. https://doi.org/10.1007/978-981-15-0936-0_46
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DOI: https://doi.org/10.1007/978-981-15-0936-0_46
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