{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:25:13Z","timestamp":1743009913385,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819970216"},{"type":"electronic","value":"9789819970223"}],"license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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\/978-981-99-7022-3_16","type":"book-chapter","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:02:57Z","timestamp":1699574577000},"page":"172-184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GHGA-Net: Global Heterogeneous Graph Attention Network for\u00a0Chinese Short Text Classification"],"prefix":"10.1007","author":[{"given":"Meimei","family":"Li","sequence":"first","affiliation":[]},{"given":"Yuzhi","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Jiguo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shihao","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. Eprint Arxiv (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Tao, H., Tong, S., Zhao, H., Xu, T., Jin, B., Liu, Q.: A radical-aware attention-based model for Chinese text classification. In: AAAI Conference on Artificial Intelligence (2019)","DOI":"10.1609\/aaai.v33i01.33015125"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"5731","DOI":"10.1007\/s10462-022-10144-1","volume":"55","author":"M Wankhade","year":"2022","unstructured":"Wankhade, M., Rao, A.C.S., Kulkarni, C.: A survey on sentiment analysis methods, applications, and challenges. Artif. Intell. Rev. 55, 5731\u20135780 (2022)","journal-title":"Artif. Intell. Rev."},{"key":"16_CR4","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS (2016)"},{"key":"16_CR5","first-page":"1","volume":"39","author":"L Hu","year":"2019","unstructured":"Hu, L., Yang, T., Shi, C., Ji, H., Li, X.: Heterogeneous graph attention networks for semi-supervised short text classification. ACM Trans. Inf. Syst. (TOIS) 39, 1\u201329 (2019)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"16_CR6","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: European Conference on Artificial Intelligence (2020)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, S., Yao, Q., Dou, D.: Hierarchical heterogeneous graph representation learning for short text classification. arXiv e-prints (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.247"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Zheng, K., Wang, Y., Yao, Q., Dou, D.: Simplified graph learning for inductive short text classification. In: Conference on Empirical Methods in Natural Language Processing (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.735"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Xu, B., Xu, J., Yang, L., Li, C., Xu, B.: Compositional recurrent neural networks for Chinese short text classification. 2016 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI), pp. 137\u2013144 (2016)","DOI":"10.1109\/WI.2016.0029"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.H.: Hierarchical attention networks for document classification. In: North American Chapter of the Association for Computational Linguistics (2016)","DOI":"10.18653\/v1\/N16-1174"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. ArXiv arXiv:1805.02023 (2018)","DOI":"10.18653\/v1\/P18-1144"},{"key":"16_CR12","unstructured":"Xu, L., et al.: Fewclue: a Chinese few-shot learning evaluation benchmark. ArXiv arXiv:2107.07498 (2021)"},{"key":"16_CR13","unstructured":"Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. ArXiv arXiv:1809.05679 (2018)"},{"key":"16_CR14","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio\u2019, P., Bengio, Y.: Graph attention networks. ArXiv arXiv:1710.10903 (2017)"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Ding, K., Wang, J., Li, J., Li, D., Liu, H.: Be more with less: hypergraph attention networks for inductive text classification. In: Conference on Empirical Methods in Natural Language Processing (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"16_CR16","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. ArXiv arXiv:1810.04805 (2019)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., Liu, Q.: ERNIE: enhanced language representation with informative entities. In: Annual Meeting of the Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1139"},{"key":"16_CR18","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. ArXiv arXiv:1907.11692 (2019)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Mining Text Data (2012)","DOI":"10.1007\/978-1-4614-3223-4"},{"key":"16_CR20","first-page":"397","volume":"30","author":"A Tamekuri","year":"2022","unstructured":"Tamekuri, A., Nakamura, K., Takahashi, Y., Yamaguchi, S.: Providing interpretability of document classification by deep neural network with self-attention. J. Inf. Process. 30, 397\u2013410 (2022)","journal-title":"J. Inf. Process."},{"key":"16_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NIPS (2017)"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2023: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7022-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:10:56Z","timestamp":1699575056000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7022-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"ISBN":["9789819970216","9789819970223"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7022-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"10 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"15 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"422","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"95","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}