{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:20:01Z","timestamp":1750220401142,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,2,11]]},"DOI":"10.1145\/3488560.3498430","type":"proceedings-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T21:42:57Z","timestamp":1644961377000},"page":"1063-1071","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Sequence-to-Sequence Model for Large-scale Chinese Abbreviation Database Construction"],"prefix":"10.1145","author":[{"given":"Chao","family":"Wang","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Jingping","family":"Liu","sequence":"additional","affiliation":[{"name":"East China University of Science and Technology, Shanghai, China"}]},{"given":"Tianyi","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Jiahang","family":"Li","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Juntao","family":"Liu","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Yanghua","family":"Xiao","sequence":"additional","affiliation":[{"name":"Fudan University & Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Rui","family":"Xie","sequence":"additional","affiliation":[{"name":"Meituan, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)."},{"volume-title":"English word-formation","author":"Bauer Laurie","key":"e_1_3_2_2_2_1","unstructured":"Laurie Bauer. 1983. English word-formation .Cambridge university press."},{"key":"e_1_3_2_2_3_1","volume-title":"Proceedings of the Third SIGHAN Workshop on Chinese Language Processing .","author":"Chang Jing-Shin","year":"2004","unstructured":"Jing-Shin Chang and Yu-Tso Lai. 2004. A preliminary study on probabilistic models for Chinese abbreviations. In Proceedings of the Third SIGHAN Workshop on Chinese Language Processing ."},{"key":"e_1_3_2_2_4_1","first-page":"3","article-title":"Mining atomic Chinese abbreviations with a probabilistic single character recovery model","volume":"40","author":"Chang Jing-Shin","year":"2006","unstructured":"Jing-Shin Chang and Wei-Lun Teng. 2006. Mining atomic Chinese abbreviations with a probabilistic single character recovery model. Language Resources and Evaluation , Vol. 40, 3--4 (2006), 367--374.","journal-title":"Language Resources and Evaluation"},{"key":"e_1_3_2_2_5_1","unstructured":"Huan Chen Qi Zhang Jin Qian and Xuanjing Huang. 2013. Chinese Named Entity Abbreviation Generation Using First-Order Logic. In IJCNLP ."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016252"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271809"},{"key":"e_1_3_2_2_8_1","volume-title":"2019 b. Online sales prediction via trend alignment-based multitask recurrent neural networks. Knowledge and Information Systems","author":"Chen Tong","year":"2019","unstructured":"Tong Chen, Hongzhi Yin, Hongxu Chen, Hao Wang, Xiaofang Zhou, and Xue Li. 2019 b. Online sales prediction via trend alignment-based multitask recurrent neural networks. Knowledge and Information Systems (2019), 1--29."},{"key":"e_1_3_2_2_9_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)."},{"key":"e_1_3_2_2_10_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 (2018)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-020-01488-7"},{"key":"e_1_3_2_2_12_1","volume-title":"Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking. arXiv preprint arXiv:2107.08173","author":"Geng Binzong","year":"2021","unstructured":"Binzong Geng, Fajie Yuan, Qiancheng Xu, Ying Shen, Ruifeng Xu, and Min Yang. 2021. Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking. arXiv preprint arXiv:2107.08173 (2021)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2904996"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-020-01515-7"},{"key":"e_1_3_2_2_16_1","volume-title":"Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025","author":"Luong Minh-Thang","year":"2015","unstructured":"Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1344"},{"key":"e_1_3_2_2_18_1","volume-title":"Generating lead sheets with affect: A novel conditional seq2seq framework. arXiv preprint arXiv:2104.13056","author":"Makris Dimos","year":"2021","unstructured":"Dimos Makris, Kat R Agres, and Dorien Herremans. 2021. Generating lead sheets with affect: A novel conditional seq2seq framework. arXiv preprint arXiv:2104.13056 (2021)."},{"key":"e_1_3_2_2_19_1","volume-title":"Efficient Estimation of Word Representations in Vector Space. CoRR","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. CoRR , Vol. abs\/1301.3781 (2013)."},{"key":"e_1_3_2_2_20_1","volume-title":"Neural machine translation and sequence-to-sequence models: A tutorial. arXiv preprint arXiv:1703.01619","author":"Neubig Graham","year":"2017","unstructured":"Graham Neubig. 2017. Neural machine translation and sequence-to-sequence models: A tutorial. arXiv preprint arXiv:1703.01619 (2017)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207548808947840"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Rohit Prabhavalkar Kanishka Rao Tara N Sainath Bo Li Leif Johnson and Navdeep Jaitly. 2017. A Comparison of Sequence-to-Sequence Models for Speech Recognition.. In Interspeech . 939--943.","DOI":"10.21437\/Interspeech.2017-233"},{"key":"e_1_3_2_2_23_1","volume-title":"Matthieu Felix, Sarguna Janani Padmanabhan, and Graham Neubig.","author":"Qi Ye","year":"2018","unstructured":"Ye Qi, Devendra Singh Sachan, Matthieu Felix, Sarguna Janani Padmanabhan, and Graham Neubig. 2018. When and why are pre-trained word embeddings useful for neural machine translation? arXiv preprint arXiv:1804.06323 (2018)."},{"key":"e_1_3_2_2_24_1","volume-title":"Bi-directional block self-attention for fast and memory-efficient sequence modeling. arXiv preprint arXiv:1804.00857","author":"Shen Tao","year":"2018","unstructured":"Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang. 2018. Bi-directional block self-attention for fast and memory-efficient sequence modeling. arXiv preprint arXiv:1804.00857 (2018)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11964"},{"key":"e_1_3_2_2_26_1","unstructured":"Xu Sun Wenjie Li Fanqi Meng and Houfeng Wang. 2013. Generalized Abbreviation Prediction with Negative Full Forms and Its Application on Improving Chinese Web Search. In IJCNLP ."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.3115\/1690219.1690273"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Xu Sun Houfeng Wang and Yu Zhang. 2006. Chinese Abbreviation-Definition Identification: A SVM Approach Using Context Information. In PRICAI .","DOI":"10.1007\/978-3-540-36668-3_53"},{"key":"e_1_3_2_2_29_1","unstructured":"Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems. 3104--3112."},{"key":"e_1_3_2_2_30_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008."},{"key":"e_1_3_2_2_31_1","unstructured":"Bing Xia Ruizhi Chu HongVE Yu Zuoshuang Zhang and Yin Gu. 2021. Abbreviation of Chinese Botanical Garden Names. In Phytohortology . EDP Sciences xvii--xx."},{"volume-title":"CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System","author":"Xu Bo","key":"e_1_3_2_2_32_1","unstructured":"Bo Xu, Yong Xu, Jiaqing Liang, Chenhao Xie, Bin Liang, Wanyun Cui, and Yanghua Xiao. 2017. CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System. In IEA\/AIE ."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.bionlp-1.29"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Dong Yang Yi-Cheng Pan and Sadaoki Furui. 2009. Automatic Chinese Abbreviation Generation Using Conditional Random Field. In HLT-NAACL .","DOI":"10.3115\/1620853.1620929"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2011.12.002"},{"key":"e_1_3_2_2_36_1","volume-title":"Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting. arXiv preprint arXiv:2107.01528","author":"Ye Jiexia","year":"2021","unstructured":"Jiexia Ye, Furong Zheng, Juanjuan Zhao, Kejiang Ye, and Chengzhong Xu. 2021. Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting. arXiv preprint arXiv:2107.01528 (2021)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Qi Zhang Jin Qian Ya Guo Yaqian Zhou and Xuanjing Huang. 2016. Generating Abbreviations for Chinese Named Entities Using Recurrent Neural Network with Dynamic Dictionary. In EMNLP .","DOI":"10.18653\/v1\/D16-1069"},{"key":"e_1_3_2_2_38_1","volume-title":"A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction. CoRR","author":"Zhang Yi","year":"2018","unstructured":"Yi Zhang and Xu Sun. 2018. A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction. CoRR , Vol. abs\/1712.06289 (2018)."}],"event":{"name":"WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event AZ USA","acronym":"WSDM '22"},"container-title":["Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498430","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3498430","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:51Z","timestamp":1750191531000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":38,"alternative-id":["10.1145\/3488560.3498430","10.1145\/3488560"],"URL":"https:\/\/doi.org\/10.1145\/3488560.3498430","relation":{},"subject":[],"published":{"date-parts":[[2022,2,11]]},"assertion":[{"value":"2022-02-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}