Burmese Sentiment Analysis Based on Transfer Learning
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초록
Using a rich resource language to classify sentiments in a language with few resources is a popular subject ofresearch in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeledtraining data for sentiment classification in Burmese, in this study, we propose a method of transfer learningfor sentiment analysis of a language that uses the feature transfer technique on sentiments in English. Thismethod generates a cross-language word-embedding representation of Burmese vocabulary to map Burmesetext to the semantic space of English text. A model to classify sentiments in English is then pre-trained using aconvolutional neural network and an attention mechanism, where the network shares the model for sentimentanalysis of English. The parameters of the network layer are used to learn the cross-language features of thesentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model wastuned using the labeled Burmese data. The results of the experiments show that the proposed method cansignificantly improve the classification of sentiments in Burmese compared to a model trained using only aBurmese corpus.
Abstract
Using a rich resource language to classify sentiments in a language with few resources is a popular subject ofresearch in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeledtraining data for sentiment classification in Burmese, in this study, we propose a method of transfer learningfor sentiment analysis of a language that uses the feature transfer technique on sentiments in English. Thismethod generates a cross-language word-embedding representation of Burmese vocabulary to map Burmesetext to the semantic space of English text. A model to classify sentiments in English is then pre-trained using aconvolutional neural network and an attention mechanism, where the network shares the model for sentimentanalysis of English. The parameters of the network layer are used to learn the cross-language features of thesentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model wastuned using the labeled Burmese data. The results of the experiments show that the proposed method cansignificantly improve the classification of sentiments in Burmese compared to a model trained using only aBurmese corpus.
참고문헌 (22)
- J. W. Watkins, "Burmese", Journal of the International Phonetic Association, 31(2), pp.291~295 (2001)
- R. Mihalcea, "Corpus-based and knowledge-based measures of text semantic similarity", Proceedings of the 21st National Conference of Artificial Intelligence (AAAI), pp.775~780 (2006)
- B. Ohana, "Sentiment classification of reviews using SentiWordNet", Proceedings of IT&T, 11(6), pp.75~78 (2009)
- V. Hatzivassiloglou, "Predicting the semantic orientation of adjectives", 35th annual meeting of the association for computational linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, pp.174~181 (1997)
- R. Xia, "Co-training for semi-supervised sentiment classification based on dualview bags-of-words representation", Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp.1054~1063 (2015)
- Q. Chen, "Learning to adapt credible knowledge in cross-lingual sentiment analysis", Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp.419~429 (2015)
- P. Ruangkanokmas, "Deep belief networks with feature selection for sentiment classification", Proceedings of 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp.9~14 (2016)
- X. Wan, "Co-training for cross-lingual sentiment classification", Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp.235~243 (2009)
- Z. Lin, "A cross-lingual joint aspect", sentiment model for sentiment analysis, 2014, 1089~1098
- X. Chen, "Adversarial deep averaging networks for cross-lingual sentiment classification", Transactions of the Association for Computational Linguistics, 6, pp.557~570 (2018)
- X. Zhou, "Cross-lingual sentiment classification with bilingual document representation learning", Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp.1403~1412 (2016)
- H. Yan, "A unified generative framework for aspect-based sentiment analysis", Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL, 2021, 2416~2429
- L. Xu, "Learning span-level interactions for aspect sentiment triplet extraction", Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL, 2021, 4755~5766
- R. Li, "Dual graph convolutional networks for aspect-based sentiment analysis", Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp.6319~6329 (2021)
- E. Kuriyozov, "Cross-lingual word embeddings for Turkic languages", Proceedings of the 12th Language Resources and Evaluation Conference (LREC), pp.4054~4062 (2020)
- D. Wang, "Coarse alignment of topic and sentiment : a unified model for cross-lingual sentiment classification", IEEE Transactions on Neural Networks and Learning Systems, 32(2), pp.736~747 (2020)
- T. Mikolov, "Distributed representations of words and phrases and their compositionality", Advances in Neural Information Processing Systems, 26, pp.3111~3119 (2013)
- S. Li, "Semi-stacking for semi-supervised sentiment classification", Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp.27~31 (2015)
- D. Zeng, "Relation classification via convolutional deep neural network", Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers (COLING), pp.2335~2344 (2014)
- C. Zhang, "Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data", IEEE Journal on Selected Areas in Communications, 37(6), pp.1389~1401 (2019)
- A. Joulin, "Bag of tricks for efficient text classification", Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp.427~431 (2017)
- B. Rink, "UTD: classifying semantic relations by combining lexical and semantic resources", Proceedings of the 5th International Workshop on Semantic Evaluation, pp.256~259 (2010)