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Journals / Journal of Artificial Intelligence and Data Science / 2021 / Cilt: 1 - Sayı: 1

A Novel Approach for Detecting Defective Expressions in Turkish

Atilla SUNCAK
Özlem AKTAŞ
Pages
35–40
Publication Date
2021-08-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study makes a crucial contribution for Turkish in terms of semantic analysis and for further related performances.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The use of machine learning has been increasing rapidly in recent years by being more efficient in comparison to rule-based techniques.
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Key Findings & Data:However, NLP (Natural Language Processing) operations generally require language specific solutions, especially semantic problems.
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Conclusion & Impact:This study makes a crucial contribution for Turkish in terms of semantic analysis and for further related performances.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Conv1D · defective expressions · deep learning

Abstract

The use of machine learning has been increasing rapidly in recent years by being more efficient in comparison to rule-based techniques. However, NLP (Natural Language Processing) operations generally require language specific solutions, especially semantic problems. Therefore, deep learning techniques are the best approach for detecting ambiguities in Turkish sentences as they do not need rule-based code implementations. Embedding word vectors are the vectorial visualizations of texts and are beneficial to analyze the word relationships in terms of semantics. In this study, CNN (Convolutional Neural Network) model is proposed to detect defective semantic expressions in Turkish sentences, and the accuracy results of the model are decided to be analyzed. This study makes a crucial contribution for Turkish in terms of semantic analysis and for further related performances.

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Cite This Article

SUNCAK, A., AKTAŞ, Ö. (2021). A Novel Approach for Detecting Defective Expressions in Turkish. Journal of Artificial Intelligence and Data Science, Cilt: 1 - Sayı: 1, 35–40. https://ajindex.com/articles/853784

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References (12)

  1. Ö. Aktaş, Ç.C. Birant, B. Aksu and Y. Çebi, “Automated synonym dictionary generation tool for turkish (ASDICT)”, Bilig, vol. 65, pp. 47-68, March 2013
  2. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” ICLR, 2013
  3. Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," In Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998
  4. W. T. Yih, X. He, and C. Meek, “Semantic parsing for single-relation question answering,” In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 643-648, June 2014
  5. Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil, “Learning semantic representations using convolutional neural networks for web search,” In Proceedings of the 23rd international conference on world wide web, 2014
  6. K. Nal, G. Edward, and B. Phil, “A convolutional neural network for modelling sentences,” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 655-665, 2014
  7. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” Journal of Machine Learning Research, vol. 12, pp. 2493-2537, 2011
  8. F. Alessio, and A. Esuli, "An NLP approach for cross-domain ambiguity detection in requirements engineering." Automated Software Engineering, pp. 559-598, 2019 [9] M. Bano, "Addressing the challenges of requirements ambiguity: a review of empirical literature," IEEE Fifth International Workshop on Empirical Requirements Engineering (EmpiRE), pp. 21-24, 2015
  9. Y. Hoceini, M. A. Cheragui, and M. Abbas, "Towards a new approach for disambiguation in NLP by multiple criterian decisionaid," Prague Bull. Math. Linguistics, pp. 19-32, 2011
  10. M. Hussain, J.J. Bird, and D.R. Faria. “A study on cnn transfer learning for image classification,” 18th Annual UK Workshop on Computational Intelligence, UKCI, 2018
  11. D. S. Dewantara, I. Budi, and M. O. Ibrohim, “3218IR at semEval-2020 task 11: conv1D and word embedding in propaganda span identification at news articles,” In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 1716-1721, 2020
  12. K. Yoon, “Convolutional neural networks for sentence classification,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, ‘08, 2014 [14] K. Radhika, K. R. Bindu, and P. Latha, “A t