Parsing struktur semantik soal cerita matematika berbahasa Indonesia menggunakan recursive neural network

Translated title of the contribution: Parsing the semantic structure of Indonesian math word problems using the recursive neural network

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Math word problems play an important role for the development of artificial intelligent. This is because solving word problems involves the development of a system that can understand natural language. Designing a system for solving math word problems requires a mechanism for decomposing a text into segments of text to be translated into math operation. The segments are categorized through the process of parsing the semantic structure of the word problems to obtain segments whose meanings refer to math operation. A number of current proposed methods are suitable to be applied to English math word problems and have never been applied to Indonesian math word problems. The impact is that the segments produced are not necessarily in line with the sequences of operations appropriate with the meaning of the story. This study proposed the use of Recursive Neural Network (RNN) as a parser of semantic structure of Indonesian math word problems. The testing of the parser was carried out on the math word problems taken from the Elementary School’s Electronic School Book (BSE) published by the Book Center of the Ministry of Education and Culture. The result of the testing showed that the final accuracy was 86.4%.

Translated title of the contributionParsing the semantic structure of Indonesian math word problems using the recursive neural network
Original languageIndonesian
Pages (from-to)106-115
Number of pages10
JournalRegister: Jurnal Ilmiah Teknologi Sistem Informasi
Volume5
Issue number2
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Binary tree
  • Math word problem
  • Parsing
  • Recursive Neural Network
  • Semantic structure

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