{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:04:48Z","timestamp":1743048288957,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819723027"},{"type":"electronic","value":"9789819723034"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-2303-4_17","type":"book-chapter","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T08:02:03Z","timestamp":1716883323000},"page":"252-267","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Training Graph Neural Network for\u00a0the\u00a0Bidding Project Title Short Text Classification"],"prefix":"10.1007","author":[{"given":"Shengnan","family":"Li","sequence":"first","affiliation":[]},{"given":"Xiaoming","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiangzhi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xuqiang","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Liu, J., Jiao, Y., Wang, Y., Li, H., Zhang, X., Cui, G.: Research on the application of DNA cryptography in electronic bidding system. In: Pan, L., Liang, J., Qu, B. (eds.) Bio-inspired Computing: Theories and Applications, BIC-TA 2019. CCIS, vol. 1160, pp. 221\u2013230. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-3415-7_18","DOI":"10.1007\/978-981-15-3415-7_18"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Karl, F., Scherp, A.: Transformers are short text classifiers: a study of inductive short text classifiers on benchmarks and real-world datasets. arXiv preprint arXiv:2211.16878 (2022)","DOI":"10.1007\/978-3-031-40837-3_7"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Song, G., Ye, Y., Du, X., Huang, X., Bie, S.: Short text classification: a survey. J. Multimedia 9(5) (2014)","DOI":"10.4304\/jmm.9.5.635-643"},{"issue":"20","key":"17_CR4","doi-asserted-by":"publisher","first-page":"4031","DOI":"10.1016\/j.ins.2010.06.021","volume":"180","author":"L Wenyin","year":"2010","unstructured":"Wenyin, L., Quan, X., Feng, M., Qiu, B.: A short text modeling method combining semantic and statistical information. Inf. Sci. 180(20), 4031\u20134041 (2010)","journal-title":"Inf. Sci."},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Linmei, H., Yang, T., Shi, C., Ji, H., Li, X.: Heterogeneous graph attention networks for semi-supervised short text classification. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4821\u20134830 (2019)","DOI":"10.18653\/v1\/D19-1488"},{"issue":"2","key":"17_CR6","first-page":"1","volume":"13","author":"Q Li","year":"2022","unstructured":"Li, Q., et al.: A survey on text classification: from traditional to deep learning. ACM Trans. Intell. Syst. Technol. (TIST) 13(2), 1\u201341 (2022)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"3","key":"17_CR7","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1007\/s11280-022-01029-y","volume":"25","author":"H Yin","year":"2022","unstructured":"Yin, H., Song, X., Yang, S., Li, J.: Sentiment analysis and topic modeling for covid-19 vaccine discussions. World Wide Web 25(3), 1067\u20131083 (2022)","journal-title":"World Wide Web"},{"issue":"1","key":"17_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10472-018-9612-z","volume":"85","author":"G Jain","year":"2019","unstructured":"Jain, G., Sharma, M., Agarwal, B.: Spam detection in social media using convolutional and long short term memory neural network. Ann. Math. Artif. Intell. 85(1), 21\u201344 (2019)","journal-title":"Ann. Math. Artif. Intell."},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Zha, W., et al.: Forecasting monthly gas field production based on the CNN-LSTM model. Energy 124889 (2022)","DOI":"10.1016\/j.energy.2022.124889"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Gaafar, A.S., Dahr, J.M., Hamoud, A.K.: Comparative analysis of performance of deep learning classification approach based on LSTM-RNN for textual and image datasets. Informatica 46(5) (2022)","DOI":"10.31449\/inf.v46i5.3872"},{"key":"17_CR11","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. CoRR abs\/1408.5882 (2014). http:\/\/arxiv.org\/abs\/1408.5882"},{"issue":"8","key":"17_CR12","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a029 (2015)","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"17_CR14","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"17_CR15","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. arXiv preprint arXiv:1803.02155 (2018)","DOI":"10.18653\/v1\/N18-2074"},{"key":"17_CR17","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Xia, J., Li, M., Tang, Y., Yang, S.: Course map learning with graph convolutional network based on AuCM. World Wide Web 1\u201320 (2023)","DOI":"10.1007\/s11280-023-01194-8"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 7370\u20137377 (2019)","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"17_CR20","unstructured":"Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861\u20136871. PMLR (2019)"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Liu, X., You, X., Zhang, X., Wu, J., Lv, P.: Tensor graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 8409\u20138416 (2020)","DOI":"10.1609\/aaai.v34i05.6359"},{"issue":"12","key":"17_CR22","doi-asserted-by":"publisher","first-page":"352","DOI":"10.3390\/a14120352","volume":"14","author":"K Zhao","year":"2021","unstructured":"Zhao, K., Huang, L., Song, R., Shen, Q., Xu, H.: A sequential graph neural network for short text classification. Algorithms 14(12), 352 (2021)","journal-title":"Algorithms"},{"issue":"5","key":"17_CR23","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.dcan.2021.10.003","volume":"8","author":"S Peng","year":"2022","unstructured":"Peng, S., et al.: A survey on deep learning for textual emotion analysis in social networks. Digit. Commun. Netw. 8(5), 745\u2013762 (2022)","journal-title":"Digit. Commun. Netw."},{"issue":"2","key":"17_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102798","volume":"59","author":"H Chen","year":"2022","unstructured":"Chen, H., Wu, L., Chen, J., Lu, W., Ding, J.: A comparative study of automated legal text classification using random forests and deep learning. Inf. Process. Manag. 59(2), 102798 (2022)","journal-title":"Inf. Process. Manag."},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Hajibabaee, P., et al.: Offensive language detection on social media based on text classification. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0092\u20130098. IEEE (2022)","DOI":"10.1109\/CCWC54503.2022.9720804"},{"issue":"1","key":"17_CR26","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Networks"},{"key":"17_CR27","unstructured":"Ye, Z., Jiang, G., Liu, Y., Li, Z., Yuan, J.: Document and word representations generated by graph convolutional network and bert for short text classification. In: ECAI 2020, pp. 2275\u20132281. IOS Press (2020)"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Huang, L., Ma, D., Li, S., Zhang, X., Wang, H.: Text level graph neural network for text classification. arXiv preprint arXiv:1910.02356 (2019)","DOI":"10.18653\/v1\/D19-1345"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Phan, X.H., Nguyen, L.M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proceedings of the 17th International Conference on World Wide Web, pp. 91\u2013100 (2008)","DOI":"10.1145\/1367497.1367510"},{"key":"17_CR30","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Galke, L., Scherp, A.: Bag-of-words vs. graph vs. sequence in text classification: questioning the necessity of text-graphs and the surprising strength of a wide MLP. arXiv preprint arXiv:2109.03777 (2021)","DOI":"10.18653\/v1\/2022.acl-long.279"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Shen, D., et al.: Baseline needs more love: on simple word-embedding-based models and associated pooling mechanisms. arXiv preprint arXiv:1805.09843 (2018)","DOI":"10.18653\/v1\/P18-1041"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)","DOI":"10.18653\/v1\/E17-2068"},{"key":"17_CR34","unstructured":"Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101 (2016)"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2303-4_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T08:05:18Z","timestamp":1716883518000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2303-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819723027","9789819723034"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2303-4_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apweb-waim2023.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}