{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:40:02Z","timestamp":1755862802927,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Ministry of Education of Humanities and Social Science Project","award":["23YJAZH220"],"award-info":[{"award-number":["23YJAZH220"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation of China","award":["2023A1515012718"],"award-info":[{"award-number":["2023A1515012718"]}]},{"name":"Philosophy and Social Sciences 14th Five-Year Plan Project of Guangdong Province","award":["GD23CTS03"],"award-info":[{"award-number":["GD23CTS03"]}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376062"],"award-info":[{"award-number":["62376062"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,2,2]]},"DOI":"10.1145\/3651671.3651736","type":"proceedings-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T18:55:50Z","timestamp":1717786550000},"page":"587-593","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An Aspect Sentiment Triplet Extraction Method based on Syntax-Guided Muti-Turn Machine Reading Comprehension"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2661-3078","authenticated-orcid":false,"given":"Zhou Yong","family":"Mei","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Guangdong University of Foreign Studies, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2605-2871","authenticated-orcid":false,"given":"Huang Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Guangdong University of Foreign Studies, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6338-0299","authenticated-orcid":false,"given":"Wang Ji","family":"Gang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3762-1379","authenticated-orcid":false,"given":"Wang Ze","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Guangdong University of Foreign Studies, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-8347","authenticated-orcid":false,"given":"Zhou","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Guangdong University of Foreign Studies, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1751-4801","authenticated-orcid":false,"given":"Yang Ai","family":"Min","sequence":"additional","affiliation":[{"name":"School of Computer Science and Intelligence Education, Lingnan Normal University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. 1346\u20131356","author":"Liu Kang","year":"2012","unstructured":"Kang Liu, Liheng Xu, and Jun Zhao. 2012. Opinion target extraction using word-based translation model. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. 1346\u20131356."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Hu Xu Bing Liu Lei Shu and Philip S Yu. 2018. Double embeddings and CNN-based sequence labeling for aspect extraction. arXiv preprint arXiv:1805.04601.","DOI":"10.18653\/v1\/P18-2094"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Xin Li Lidong Bing Piji Li Wai Lam and Zhimou Yang. 2018. Aspect term extraction with history attention and selective transformation. arXiv preprint arXiv:1805.00760.","DOI":"10.24963\/ijcai.2018\/583"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1344"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1259"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6469"},{"key":"e_1_3_2_1_7_1","unstructured":"Dehong Ma Sujian Li Xiaodong Zhang and Houfeng Wang. 2017. Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1569"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6383"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17500"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Shaonan Wang Jiajun Zhang and Chengqing Zong. 2016. Learning sentence representation with guidance of human attention. arXiv preprint arXiv:1609.09189.","DOI":"10.24963\/ijcai.2017\/578"},{"key":"e_1_3_2_1_12_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Pramod Kaushik Mudrakarta Ankur Taly Mukund Sundararajan and Kedar Dhamdhere. 2018. Did the model understand the question? arXiv preprint arXiv:1805.05492.","DOI":"10.18653\/v1\/P18-1176"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Lu Xu Hao Li Wei Lu and Lidong Bing. 2020. Position-aware tagging for aspect sentiment triplet extraction. arXiv preprint arXiv:2010.02609.","DOI":"10.18653\/v1\/2020.emnlp-main.183"},{"key":"e_1_3_2_1_15_1","unstructured":"Zhen Wu Chengcan Ying Fei Zhao Zhifang Fan Xinyu Dai and Rui Xia. 2020. Grid tagging scheme for aspect-oriented fine-grained opinion extraction. arXiv preprint arXiv:2010.04640."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Chen Zhang Qiuchi Li Dawei Song and Benyou Wang. 2020. A multi-task learning framework for opinion triplet extraction. arXiv preprint arXiv:2010.01512.","DOI":"10.18653\/v1\/2020.findings-emnlp.72"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Zhexue Chen Hong Huang Bang Liu Xuanhua Shi and Hai Jin. 2021. Semantic and syntactic enhanced aspect sentiment triplet extraction. arXiv preprint arXiv:2106.03315.","DOI":"10.18653\/v1\/2021.findings-acl.128"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Hang Yan Junqi Dai Xipeng Qiu Zheng Zhang 2021. A unified generative framework for aspect-based sentiment analysis. arXiv preprint arXiv:2106.04300.","DOI":"10.18653\/v1\/2021.acl-long.188"},{"key":"e_1_3_2_1_19_1","unstructured":"Xiaoya Li Jingrong Feng Yuxian Meng Qinghong Han Fei Wu and Jiwei Li. 2019. A unified MRC framework for named entity recognition. arXiv preprint arXiv:1910.11476."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.figlang-1.4"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17597"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics. 2461\u20132471","author":"Yang Yifei","year":"2022","unstructured":"Yifei Yang and Hai Zhao. 2022. Aspect-based Sentiment Analysis as Machine Reading Comprehension. In Proceedings of the 29th International Conference on Computational Linguistics. 2461\u20132471."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.212"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Omer Levy Minjoon Seo Eunsol Choi and Luke Zettlemoyer. 2017. Zero-shot relation extraction via reading comprehension. arXiv preprint arXiv:1706.04115.","DOI":"10.18653\/v1\/K17-1034"},{"key":"e_1_3_2_1_25_1","volume-title":"Qanet: Combining local convolution with global self-attention for reading comprehension. arXiv preprint arXiv:1804.09541.","author":"Yu Adams Wei","year":"2018","unstructured":"Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le. 2018. Qanet: Combining local convolution with global self-attention for reading comprehension. arXiv preprint arXiv:1804.09541."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Suzana Ili\u0107 Edison Marrese-Taylor Jorge A Balazs and Yutaka Matsuo. 2018. Deep contextualized word representations for detecting sarcasm and irony. arXiv preprint arXiv:1809.09795.","DOI":"10.18653\/v1\/W18-6202"},{"key":"e_1_3_2_1_27_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805."},{"key":"e_1_3_2_1_28_1","unstructured":"Xiaoya Li Jingrong Feng Yuxian Meng Qinghong Han Fei Wu and Jiwei Li. 2019. A unified MRC framework for named entity recognition. arXiv preprint arXiv:1910.11476."},{"key":"e_1_3_2_1_29_1","unstructured":"Xiaoya Li Fan Yin Zijun Sun Xiayu Li Arianna Yuan Duo Chai Mingxin Zhou and Jiwei Li. 2019. Entity-relation extraction as multi-turn question answering. arXiv preprint arXiv:1905.05529."},{"key":"e_1_3_2_1_30_1","volume-title":"Caiming Xiong, and Richard Socher.","author":"McCann Bryan","year":"2018","unstructured":"Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018. The natural language decathlon:Multitask learning as question answering. arXiv preprint arXiv:1806.08730."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6511"},{"key":"e_1_3_2_1_32_1","volume-title":"Orph\u00e9e De Clercq","author":"Pontiki Maria","year":"2016","unstructured":"Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammed AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orph\u00e9e De Clercq, 2016. Semeval-2016 task 5: Aspect based sentiment analysis. In ProWorkshop on Semantic Evaluation (SemEval-2016). Association for Computational Linguistics, 19\u201330."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S15-2082"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"DK Kirange Ratnadeep R Deshmukh and MDK Kirange. 2014. Aspect based sentiment analysis semeval-2014 task 4. Asian Journal of Computer Science and Information Technology (AJCSIT) Vol 4.","DOI":"10.15520\/ajcsit.v4i8.9"},{"key":"e_1_3_2_1_35_1","volume-title":"Constituency parsing with a self-attentive encoder. arXiv preprint arXiv:1805.01052","author":"Kitaev Nikita","year":"2018","unstructured":"Nikita Kitaev and Dan Klein. 2018. Constituency parsing with a self-attentive encoder. arXiv preprint arXiv:1805.01052 (2018)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Junru Zhou and Hai Zhao. 2019. Head-driven phrase structure grammar parsing on Penn treebank. arXiv preprint arXiv:1907.02684.","DOI":"10.18653\/v1\/P19-1230"},{"key":"e_1_3_2_1_37_1","unstructured":"Ilya Loshchilov and Frank Hutter. 2018. Fixing weight decay regularization in adam. CoRR abs\/1711.05101"}],"event":{"name":"ICMLC 2024: 2024 16th International Conference on Machine Learning and Computing","acronym":"ICMLC 2024","location":"Shenzhen China"},"container-title":["Proceedings of the 2024 16th International Conference on Machine Learning and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3651671.3651736","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3651671.3651736","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:19:15Z","timestamp":1755861555000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3651671.3651736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,2]]},"references-count":37,"alternative-id":["10.1145\/3651671.3651736","10.1145\/3651671"],"URL":"https:\/\/doi.org\/10.1145\/3651671.3651736","relation":{},"subject":[],"published":{"date-parts":[[2024,2,2]]},"assertion":[{"value":"2024-06-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}