{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T14:41:07Z","timestamp":1790779267091,"version":"4.1.0"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:00:00Z","timestamp":1650412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Xinjiang, China","award":["2021D01C076"],"award-info":[{"award-number":["2021D01C076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>A question answering (QA) system is a research direction in the field of artificial intelligence and natural language processing (NLP) that has attracted much attention and has broad development prospects. As one of the main components in the QA system, the accuracy of question classification plays a key role in the entire QA task. Therefore, not only the traditional machine learning methods but also today\u2019s deep learning methods are widely used and deeply studied in question classification tasks. This paper mainly introduces our work on two aspects of Chinese question classification. The first is to use an answer-driven method to build a richer Chinese question classification dataset for the small-scale problems of the existing experimental dataset, which has a certain reference value for the expansion of the dataset, especially for the construction of those low-resource language datasets. The second is to propose a deep learning model of problem classification with a Transformer + Bi-GRU + Attention structure. Transformer has strong learning and coding ability, but it adopts the scheme of fixed coding length, which divides the long text into multiple segments, and each segment is coded separately; there is no interaction that occurs between segments. Here, we achieve the information interaction between segments through Bi-GRU so as to improve the coding effect of long sentences. Our purpose of adding the Attention mechanism is to highlight the key semantics in questions that contain answers. The experimental results show that the model proposed in this paper has significantly improved the accuracy of question classification.<\/jats:p>","DOI":"10.3390\/info13050214","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T01:55:51Z","timestamp":1650506151000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Attention-Based Transformer-BiGRU for Question Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Dongfang","family":"Han","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1639-8899","authenticated-orcid":false,"given":"Turdi","family":"Tohti","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-308X","authenticated-orcid":false,"given":"Askar","family":"Hamdulla","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,20]]},"reference":[{"key":"ref_1","unstructured":"(2022, March 02). Data Age 2025 of IDC. Available online: https:\/\/www.seagate.com\/cn\/zh\/our-story\/data-age-2025\/."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Green, B.F., Wolf, A.K., Chomsky, C., and Laughery, K. (1961, January 9\u201311). Baseball: An Automatic Question Answerer. Proceedings of the Western Joint IRE-AIEE-ACM Computer Conference, New York, NY, USA.","DOI":"10.1145\/1460690.1460714"},{"key":"ref_3","first-page":"521","article-title":"Lunar rocks in natural English: Explorations in natural language question answering","volume":"5","author":"Woods","year":"1977","journal-title":"Linguist. Struct. Processing"},{"key":"ref_4","first-page":"35","article-title":"The Berkeley UNIX consultant project","volume":"14","author":"Wilensky","year":"1988","journal-title":"Comput. Linguist."},{"key":"ref_5","first-page":"3104","article-title":"Sequence to Sequence Learning with Neural Networks","volume":"2","author":"Sutskever","year":"2014","journal-title":"Comput. Lang."},{"key":"ref_6","first-page":"25","article-title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","volume":"19","author":"Cho","year":"2014","journal-title":"Comput. Sci."},{"key":"ref_7","first-page":"61","article-title":"Neural Machine Translation by Jointly Learning to Align and Translate","volume":"20","author":"Bahdanau","year":"2014","journal-title":"Comput. Sci."},{"key":"ref_8","unstructured":"Sarker, S., Monisha, S.T.A., and Nahid, M.M.H. (2019, January 3\u20135). Classification of Bengali Questions towards a Factoid Question Answering System. Proceedings of the International Conference on Advances in Science Engineering and Robotics Technology, Dhaka, Bangladesh."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1145\/763693.763694","article-title":"Performance issues and error analysis in an open-domain question answering system","volume":"21","author":"Moldovan","year":"2003","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_10","first-page":"24","article-title":"Question Classification and Answer Extraction for Developing a Cooking QA System","volume":"24","author":"Faiz","year":"2020","journal-title":"Comput. Sist."},{"key":"ref_11","unstructured":"Jia, K., Fan, X.Z., and Xu, J.Z. (2008). Chinese qusetion classification based on KNN. Microellectronics Comput., 162\u2013164."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hacioglu, K., and Ward, W. (2003, January 27). Question Classification with Support Vector Machines and Error Correcting Codes. Proceedings of the Companion Volume of the Proceedings of HLT-NAACL 2003-Short Papers, Edmonton, AB, Canada.","DOI":"10.3115\/1073483.1073493"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10462-010-9188-4","article-title":"From symbolic to sub-symbolic information in question classification","volume":"35","author":"Silva","year":"2011","journal-title":"Artif. Intell. Rev."},{"key":"ref_14","first-page":"35","article-title":"Syntactic structure parsing based chinese question classification","volume":"20","author":"Wen","year":"2006","journal-title":"J. Chin. Inf. Processing"},{"key":"ref_15","first-page":"1058","article-title":"Research progress of open domain question answering technology","volume":"37","author":"Zhang","year":"2009","journal-title":"J. Electron."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hovy, E., Gerber, L., Hermjakob, U., Lin, C.-Y., and Ravichandran, D. (2001, January 18\u201322). Toward Semantics-Based Answer Pinpointing. Proceedings of the Second International Conference on Human Language Technology Research, San Diego, CA, USA.","DOI":"10.3115\/1072133.1072221"},{"key":"ref_17","unstructured":"Zhang, D., and Lee, W.S. (August, January 28). Question Classification Using Support Vector Machines. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, ON, Canada."},{"key":"ref_18","unstructured":"Sundblad, H.A. (2007, January 24\u201326). Re-Examination of Question Classification. Proceedings of the 16th Nordic Conference of Computational Linguistics, Tartu, Estonia."},{"key":"ref_19","first-page":"535","article-title":"Combination classifier based on error- driven algorithm and its application in problem classification","volume":"45","author":"Li","year":"2008","journal-title":"J. Comput. Res. Dev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, Z., Thint, M., and Qin, Z. (2008, January 25\u201327). Question Classification Using Head Words and Their Hypernyms. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Honolulu, HI, USA.","DOI":"10.3115\/1613715.1613835"},{"key":"ref_21","unstructured":"Peng, X.Y., and Zhong, D. (2020). Survey of Cross-Lingual Word Embedding. J. Chin. Inf. Processing, 34."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the Empirical Methods in Natural Language Processing, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_23","unstructured":"Komninos, A., and Manandhar, S. (2015, January 26\u201331). Dependency Based Embeddings for Sentence Classification Tasks. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China."},{"key":"ref_24","first-page":"171","article-title":"Research on question classification via bilingual information","volume":"31","author":"Xu","year":"2017","journal-title":"J. Chin. Inf. Processing"},{"key":"ref_25","first-page":"157","article-title":"Community Q&A question classification method based on Bi-LSTM and CNN and including attention mechanis","volume":"27","author":"Shi","year":"2018","journal-title":"Comput. Syst. Appl."},{"key":"ref_26","first-page":"25","article-title":"Classification of Chinese Questions Based on Support Vector Machine","volume":"33","author":"Yu","year":"2005","journal-title":"J. South China Univ. Technol. Nat. Sci. Ed."},{"key":"ref_27","first-page":"75","article-title":"Question classification based on self-learning rules and modified Bayes","volume":"27","author":"Tian","year":"2010","journal-title":"Appl. Res. Comput."},{"key":"ref_28","first-page":"111","article-title":"Chinese question classification based on Chinese Frame Net","volume":"45","author":"Li","year":"2009","journal-title":"Comput. Eng. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S.R. (2018). GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. arXiv.","DOI":"10.18653\/v1\/W18-5446"},{"key":"ref_30","unstructured":"Liu, Y.H., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv."},{"key":"ref_31","unstructured":"Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). DistilBERT, a Distilled Version of BERT: Smaller, Faster, Cheaper and Lighter. arXiv."},{"key":"ref_32","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q.V. (2019, January 10\u201312). Xlnet: Generalized autoregressive pretraining for language understanding. Proceedings of the 33th Conference and Workshop on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_33","unstructured":"Ezen, C.A. (2020). A Comparison of LSTM and BERT for Small Corpus. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Khandelwal, U., He, H., Qi, P., and Jurafsky, D. (2018). Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context. arXiv.","DOI":"10.18653\/v1\/P18-1027"},{"key":"ref_35","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv."},{"key":"ref_36","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2015). Distributed Representations of Words and Phrases and Their Compositionality. arXiv."},{"key":"ref_37","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA."},{"key":"ref_38","unstructured":"Chung, J., Gulcehre, C., Cho, K.H., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1002\/asi.20583","article-title":"TREC: Experiment and evaluation in information retrieval","volume":"58","year":"2007","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"ref_40","unstructured":"Choi, E., Kitzie, V., and Shah, C. (2013, January 12\u201315). A Machine Learning-Based Approach to Predicting Success of Questions on Social Question Answering. Proceedings of the iConference 2013, Fort Worth, TX, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Adamic, L.A., Zhang, J., Bakshy, E., and Ackerman, M.S. (2008, January 21\u201325). Knowledge Sharing and Yahoo Answers: Everyone Knows Something. Proceedings of the International Conference on World Wide Web ACM, Beijing, China.","DOI":"10.1145\/1367497.1367587"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/5\/214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:57:48Z","timestamp":1760137068000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/5\/214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,20]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["info13050214"],"URL":"https:\/\/doi.org\/10.3390\/info13050214","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,20]]}}}