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Research Article

STSG: A Short Text Semantic Graph Model for Similarity Computing Based on Dependency Parsing and Pre-trained Language Models

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Article: 2321552 | Received 13 Jun 2023, Accepted 07 Feb 2024, Published online: 04 Mar 2024

References

  • Achiam, J., S. Adler, S. Agarwal, L. Ahmad, I. Akkaya. 2023 Dec 19. Gpt-4 technical report. arXiv Preprint arXiv 2303:08774.
  • Alex, W., S. Amanpreet, M. Julian, H. Felix, L. Omer, and B. Samuel. 2019 Feb 22. GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv Preprint arXiv 1804:07461.
  • Ashish, V., S. Noam, P. Niki, U. Jakob, J. Llion. 2017. Attention is all you need. Proceedings of the 31st Annual Conference on Neural Information Processing Systems, Long Beach.
  • Binyuan, H., G. Ruiying, W. Lihan, Q. Bowen, L. Bowen. 2022. S2SQL: Injecting syntax to question-schema interaction graph encoder for text-to-SQL parsers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 1254–29. Dublin.
  • Chandrasekaran, D., and V. Mago. 2021. Evolution of semantic similarity—A survey. ACM Computing Surveys 54 (2):1–37. doi:10.1145/3440755.
  • Dazhi, J., W. Runguo, H. Zhihui, L. Senlin, L. Cheng, and Y. Lin. 2023. GASN: Gamma distribution test for driver genes identification based on similarity networks. Connection Science 35 (1):1–19. doi:10.1080/09540091.2023.2167937.
  • Devlin, J., M. W. Chang, K. Lee, and K. Toutanova. 2018 Oct 11. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv Preprint arXiv 1810:04805.
  • Ghafour, A., M. Jamshid Bagherzadeh, and F. Mohammad-Reza. 2022. Learning bilingual word embedding mappings with similar words in related languages using GAN. Applied Artificial Intelligence 36 (1). doi:10.1080/08839514.2021.2019885.
  • Gizem, S., Ö. Hakime, and Ö. Arzucan. 2017. BIOSSES: A semantic sentence similarity estimation system for the biomedical domain. Bioinformatics 33 (14):I49–58. doi:10.1093/bioinformatics/btx238.
  • Guimin, C., T. Yuanhe, S. Yan, and W. Xiang. 2021. Relation extraction with type-aware map memories of word dependencies. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2501–12. Bangkok.
  • He, P., X. Liu, J. Gao, and W. Chen. 2021 Oct 6. DeBERTa: Decoding-enhanced BERT with disentangled attention. arXiv Preprint arXiv 2006:03654.
  • Hironori, T., S. Junya, F. Sulfayanti, and K. Akihiro. 2022. Anomaly detection using siamese network with attention mechanism for few-shot learning. Applied Artificial Intelligence 36 (1). doi:10.1080/08839514.2022.2094885.
  • Ilya, L., and H. Frank. 2019 Jan 4. Decoupled weight decay regularization. arXiv Preprint axXiv 1711:05101.
  • Jianguo, C., L. Kenli, B. Kashif, Z. Xu, L. Keqin. 2019. A Bi-layered parallel training architecture for large-scale convolutional neural networks. IEEE Transactions on Parallel and Distributed Systems 30(5):965–76. doi:10.1109/TPDS.2018.2877359.
  • Jonas, M., and T. Aditya. 2016. Siamese recurrent architectures for learning sentence similarity. Proceedings of the AAAI conference on artificial intelligence, 2786–92. Arizona.
  • Joshi, M., D. Chen, Y. Liu, S. Weld Daniel, L. Zettlemoyer, Zettlemoyer, L. and Levy, O. 2020. SpanBERT: Improving pre-training by representing and predicting spans. Transactions of the Association for Computational Linguistics 8:64–77. doi:10.1162/tacl_a_00300.
  • Kevin, C., L. Minh-Thang, V. Quoc, and M. Christopher. 2020 May 23. ELECTRA: Pre-training text encoders as discriminators rather than generators. arXiv Preprint arXiv 2003:10555.
  • Lei Jimmy, B., K. Ryan, E. Geoffrey, H. B. Jimmy Lei. 2016 Jul 21. Layer normalization. arXiv Preprint arXiv 1607:06450.
  • Liang, Y., M. Chengsheng, and L. Yuan. 2019. Graph convolutional networks for text classification. Proceedings of the AAAI conference on artificial intelligence, 7370–77. Hawaii.
  • Lin, D. 1998. An information-theoretic definition of similarity. Proceedings of the International Conference on Machine Learning, 296–304. Madison.
  • Lingling, X., X. Haoran, W. Fu Lee, T. Xiaohui, W. Weiming, and Q. Li. 2024. Contrastive sentence representation learning with adaptive false negative cancellation. Information Fusion 102:102065–102065. doi:10.1016/j.inffus.2023.102065.
  • Mary, P., and H. Marcus. 2022 Jul 19. Formal algorithms for transformers. arXiv Preprint arXiv 2207:09238.
  • Mengting, H., Z. Xuan, Y. Xin, J. Jiahao, Y. Wei, and C. Gao. 2021. A survey on the techniques, applications, and performance of short text semantic similarity. Concurrency and Computation: Practice and Experience 33 (5). doi:10.1002/cpe.5971.
  • Minh Hieu, P., and O. Philip. 2020. Modelling context and syntactical features for aspect-based sentiment analysis. Proceedings of the 58th annual meeting of the association for computational linguistics, 3211–20. Washington.
  • Nils, R., and G. Iryna. 2019. Sentence-BERT: Sentence embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language, 3980–90. Hong Kong.
  • Paul, N., V. Maarten, and R. Mihai. 2016. Learning text similarity with siamese recurrent networks. Proceedings of the 1st Workshop on Representation Learning for NLP, 148–57. Berlin.
  • Pengcheng, H., G. Jianfeng, and C. Weizhu. 2023. DeBERTav3: Improving DeBERTa using ELECTRA-Style pre-training with gradient-disentangled embedding sharing. The Eleventh International Conference on Learning Representations, Kigali.
  • Raffel, C., N. Shazeer, A. Roberts, K. Lee, S. Narang, Matena, M., Zhou, Y., Li, W. and Liu, P.J. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21 (1):5485–551.
  • Sanh, V., L. Debut, J. Chaumond, and T. Wolf. 2019 Mar 1. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv Preprint arXiv 1910:01108.
  • Shuai, Z., W. Lijie, S. Ke, and X. Xinyan. 2020 Sep 3. A practical Chinese dependency parser based on a large-scale dataset. arXiv Preprint arXiv 2009:00901.
  • Song, C., and L. Hai. 2022. BERT-Log: Anomaly detection for system logs based on pre-trained language model. Applied Artificial Intelligence 36 (1). doi:10.1080/08839514.2022.2145642.
  • Tianyu, G., Y. Xingcheng, and C. Danqi. 2021. SimCSE - simple contrastive learning of sentence embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 6894–910. Punta Cana.
  • Velikovi, P., G. Cucurull, A. Casanova, A. Romero, P. Liò,and Bengio, Y. 2017 Feb 4. Graph attention networks. arXiv Preprint axXiv 1710:10903.
  • Weidong, Z., L. Xiaotong, J. Jun, and X. Rongchang. 2022. Re-LSTM: A long short-term memory network text similarity algorithm based on weighted word embedding. Connection Science 34 (1):2652–70. doi:10.1080/09540091.2022.2140122.
  • Wenjuan, L., S. Zhengyan, W. Subo, Z. Shunxiang, Z. Guangli, and L. Chen. 2024. PS-GCN: Psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection. Connection Science 36 (1). doi:10.1080/09540091.2023.2295820.
  • Yan, K., P. Bin, K. Yongqi, Y. Yun, C. Jianguo, and X. Xie. 2024. Two-stage perceptual quality oriented rate control algorithm for HEVC. ACM Transactions on Multimedia Computing, Communications and Applications 20 (5):1–20. doi:10.1145/3636510.
  • Yang, Z., Z. Dai, Y. Yang, G. Jaime, Salakhutdinov, R.R. and Le, Q.V. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. Proceedings of the 33rd International Conference on Neural Information Processing Systems, 5753–63. Vancouver.
  • Yangfan, L., C. Cen, D. Mingxing, Z. Zeng, and L. Kenli. 2021. Attention-aware encoder–decoder neural networks for heterogeneous graphs of things. IEEE Transactions on Industrial Informatics 17 (4):2890–98. doi:10.1109/TII.2020.3025592.
  • Yifan, P., Y. Shankai, and L. Zhiyong. 2019. Transfer learning in biomedical natural language processing: An evaluation of BERT and ELMo on ten benchmarking datasets. Proceedings of the 18th BioNLP Workshop and Shared Task, 58–65. Florence.
  • Yuanhe, T., C. Guimin, S. Yan, and W. Xiang. 2021. Dependency-driven relation extraction with attentive graph convolutional networks. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 4458–71. Bangkok.
  • Yung-Sung, C., D. Rumen, L. Hongyin, Z. Yang, C. Shiyu, Soljačić, M., Li, S.W., Yih, W.T., Kim, Y. and Glass, J., et al. 2022. DiffCSE: Difference-based contrastive learning for sentence embeddings. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4207–18. Seattle.
  • Yu, S., W. Shuohuan, L. Yukun, F. Shikun, T. Hao,Wu, H. and Wang, H. 2020. Ernie 2.0: A continual pre-training framework for language understanding. Proceedings of the AAAI conference on artificial intelligence, 8968–75. New York.
  • Zeming, L. A. Halil, R. Roshan, H. Brian, Z. Zhongkai, L. Wenting, S. Nikita, V. Robert, K. Ori, S. Yaniv, et al. 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379 (6637):1123–30.
  • Zhaorui, T., Y. Xi, Y. Zihan, W. Qiufeng, Y. Yuyao, A. Nguyen, and K. Huang. 2023. Semantic similarity distance: Towards better text-image consistency metric in text-to-image generation. Pattern Recognit 144:109883–109883. doi:10.1016/j.patcog.2023.109883.
  • Zhe, Q., W. Zhi-Jie, L. Yuquan, Y. Bin, L. Kenli, and J. Yin. 2019. An efficient framework for sentence similarity modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing 27 (4):853–65. doi:10.1109/TASLP.2019.2899494.
  • Zhiguo, W., M. Haitao, and I. Abraham. 2016 Feb 23. Sentence similarity learning by lexical decomposition and composition. arXiv Preprint axXiv 1602:07019.