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

Agung Prasetya
Chastine Fatichah
Umi Laili Yuhana

Abstract


Soal cerita berperan penting untuk kemajuan pengembangan kecerdasan buatan. Hal ini karena penyelesaian soal cerita melibatkan pengembangan sebuah sistem yang mampu memahami bahasa alami. Pembentukan sistem penyelesaian soal memerlukan mekanisme untuk mendekomposisikan teks soal ke segmen-segmen teks untuk diterjemahkan ke jenis operasi hitung. Segmen-segmen tersebut ditentukan melalui proses parsing semantik struktur soal agar menghasilkan segmen-segmen yang maknanya menunjuk operasi hitung. Sejumlah metode usulan saat ini sesuai untuk diterapkan pada soal cerita berbahasa Inggris dan belum diterapkan pada soal cerita berbahasa Indonesia. Dampaknya adalah segmen-segmen yang dihasilkan belum tentu menghasilkan urutan pengerjaan operasi yang sesuai makna cerita. Penelitian ini mengusulkan penggunaaan Recursive Neural Network (RNN) sebagai parser struktur semantik soal cerita berbahasa Indonesia. Pengujian parser struktur semantik soal dilakukan terhadap soal-soal yang berasal dari Buku Sekolah Elektronik (BSE) Sekolah Dasar (SD) dari Pusat Perbukuan Kementerian Pendidikan dan Kebudayaan. Hasil pengujian menunjukkan akurasi akhir sebesar 86,4%.

 

 

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%.


Keywords


parsing; pohon biner; Recursive Neural Network; soal cerita; struktur semantik; binary tree; math word problem; semantic structure

Full Text:

PDF

References


Clark, P. (2015). Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge! Proceedings of the Twenty-Seventh Conference on Innovative Applications of Artificial Intelligence (pp. 4019-4021). Austin, Texas, USA: AAAI Press.

Clark, P., & Etzioni, O. (2016). My Computer Is an Honor Student — but How Intelligent Is It? Standardized Tests as a Measure of AI. AI Magazine, 37(1), 5-12.

Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O., & Ang, S. D. (2015). Parsing Algebraic Word Problems into Equations. Transactions of the Association for Computational Linguistics, 3, 585-597.

Kushman, N., Artzi, Y., Zettlemoyer, L., & Barzilay, R. (2014). Learning to Automatically Solve Algebra Word Problems. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (pp. 271-281). Baltimore, Maryland, USA: Association for Computational Linguistics.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013, January 16). Efficient Estimation of Word Representations in Vector Space. Retrieved from Arxiv: https://arxiv.org/pdf/1301.3781.pdf

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 26 (NIPS 2013). Lake Tahoe, Nevada, USA: Neural Information Processing Systems (NIPS).

Mitra, A., & Baral, C. (2016). Learning To Use Formulas To Solve Simple Arithmetic Problems. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 2144-2153). Berlin, Germany: Association for Computational Linguistics.

O'Donnell, M. (2000). RSTTool 2.4 - A Markup Tool for Rhetorical Structure Theory. INLG'2000 Proceedings of the First International Conference on Natural Language Generation, (pp. 253-256).

Ratlif, N. D., Bagnell, J. A., & Zinkevich, M. A. (2006). Maximum Margin Planning. International Conference on Machine Learning. Pittsburgh, USA: International Machine Learning Society - ICML.

Roy, S., Vieira, T., & Roth, D. (2015). Reasoning about Quantities in Natural Language. Transactions of the Association for Computational Linguistics, 3, 1-13.

Socher, R., Huang, E. H., Pennington, J., Ng, A. Y., & Manning, C. D. (2011). Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. Advances in Neural Information Processing Systems 24 (NIPS 2011) (pp. 1-9). Granada, Spain: Neural Information Processing Systems (NIPS).

Socher, R., Lin, C. C.-Y., Ng, A. Y., & Manning, C. D. (2011). Parsing Natural Scenes and Natural Language with Recursive Neural Networks. International Conference on Machine Learning (ICML). Bellevue, Washington, USA: International Machine Learning Society (IMLS).

Socher, R., Pennington, J., Huang, E. H., Ng, A. Y., & Manning, C. D. (2011). Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (pp. 151-161). Edinburgh, Scotland, UK: Association for Computational Linguistics.

Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013). Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. . Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1631-1642). Seattle, Washington, USA: Association for Computational Linguistics.

Taskar, B., Klein, D., Collins, M., Koller, D., & Manning, C. (2004). Max-Margin Parsing. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing.

Upadhyay, S., Chang, M.-W., Chang, K.-W., & Yih, W.-t. (2016). Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 297-306). Austin, Texas: Association for Computational Linguistics.

Wang, L., Zhang, D., Gao, L., Song, J., Guo, L., & Shen, H. T. (2018). MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) (pp. 5545-5552). New Orleans, Louisiana, USA: AAAI Press.

Wang, Y., Liu, X., & Shi, S. (2017). Deep Neural Solver for Math Word Problems. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 845-854). Copenhagen, Denmark: Association for Computational Linguistics.

Zhou, L., Dai, S., & Chen, L. (2015 ). Learn to Solve Algebra Word Problems Using Quadratic Programming. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 817–822). Lisbon, Portugal: Association for Computational Linguistics.

Zhu, M., Zhang, Y., Chen, W., Zhang, M., & Zhu, J. (2013). Fast and Accurate Shift-Reduce Constituent Parsing. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (pp. 434-443). Sofia, Bulgaria: Association for Computational Linguistics.




DOI: https://doi.org/10.26594/register.v5i2.1537

Article metrics

Abstract views : 0 | views : 0

Refbacks

  • There are currently no refbacks.



Indexed in:

                                    


 

Creative Commons License
Register: Jurnal Ilmiah Teknologi Sistem Informasi is licensed under a Creative Commons Attribution 4.0 International License.