{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:31:54Z","timestamp":1772119914020,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":70,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DARPA KAIROS","award":["FA8750-19-2-1004"],"award-info":[{"award-number":["FA8750-19-2-1004"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-19-56151"],"award-info":[{"award-number":["IIS-19-56151"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DARPA SocialSim","award":["W911NF-17-C-0099"],"award-info":[{"award-number":["W911NF-17-C-0099"]}]},{"name":"DARPA INCAS","award":["HR001121C0165"],"award-info":[{"award-number":["HR001121C0165"]}]},{"name":"Molecule Maker Lab Institute","award":["2019897"],"award-info":[{"award-number":["2019897"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,4,25]]},"DOI":"10.1145\/3485447.3512174","type":"proceedings-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T05:13:07Z","timestamp":1650863587000},"page":"3162-3173","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification"],"prefix":"10.1145","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Zhihong","family":"Shen","sequence":"additional","affiliation":[{"name":"Microsoft, USA"}]},{"given":"Chieh-Han","family":"Wu","sequence":"additional","affiliation":[{"name":"Microsoft, USA"}]},{"given":"Boya","family":"Xie","sequence":"additional","affiliation":[{"name":"Microsoft, USA"}]},{"given":"Junheng","family":"Hao","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, USA"}]},{"given":"Ye-Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft, USA"}]},{"given":"Kuansan","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft, USA"}]},{"given":"Jiawei","family":"Han","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Rahul Agrawal Archit Gupta Yashoteja Prabhu and Manik Varma. 2013. Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages. In WWW\u201913. 13\u201324.","DOI":"10.1145\/2488388.2488391"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Iz Beltagy Kyle Lo and Arman Cohan. 2019. SciBERT: A Pretrained Language Model for Scientific Text. In EMNLP\u201919. 3615\u20133620.","DOI":"10.18653\/v1\/D19-1371"},{"key":"e_1_3_2_1_3_1","unstructured":"Duo Chai Wei Wu Qinghong Han Fei Wu and Jiwei Li. 2020. Description based text classification with reinforcement learning. In ICML\u201920. 1371\u20131382."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Ilias Chalkidis Emmanouil Fergadiotis Prodromos Malakasiotis and Ion Androutsopoulos. 2019. Large-Scale Multi-Label Text Classification on EU Legislation. In ACL\u201919. 6314\u20136322.","DOI":"10.18653\/v1\/P19-1636"},{"key":"e_1_3_2_1_5_1","unstructured":"Ming-Wei Chang Lev-Arie Ratinov Dan Roth and Vivek Srikumar. 2008. Importance of Semantic Representation: Dataless Classification.. In AAAI\u201908. 830\u2013835."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Wei-Cheng Chang Hsiang-Fu Yu Kai Zhong Yiming Yang and Inderjit\u00a0S Dhillon. 2020. Taming pretrained transformers for extreme multi-label text classification. In KDD\u201920. 3163\u20133171.","DOI":"10.1145\/3394486.3403368"},{"key":"e_1_3_2_1_7_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML\u201920. 1597\u20131607."},{"key":"e_1_3_2_1_8_1","volume-title":"SPECTER: Document-level Representation Learning using Citation-informed Transformers. In ACL\u201920. 2270\u20132282.","author":"Cohan Arman","year":"2020","unstructured":"Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel\u00a0S Weld. 2020. SPECTER: Document-level Representation Learning using Citation-informed Transformers. In ACL\u201920. 2270\u20132282."},{"key":"e_1_3_2_1_9_1","first-page":"317","article-title":"Medical subject headings used to search the biomedical literature","volume":"8","author":"Coletti H","year":"2001","unstructured":"Margaret\u00a0H Coletti and Howard\u00a0L Bleich. 2001. Medical subject headings used to search the biomedical literature. JAMIA 8, 4 (2001), 317\u2013323.","journal-title":"JAMIA"},{"key":"e_1_3_2_1_10_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT\u201919. 4171\u20134186.","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT\u201919. 4171\u20134186."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Nitesh\u00a0V Chawla and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD\u201917. 135\u2013144.","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_1_12_1","unstructured":"Steven\u00a0Y. Feng Varun Gangal Jason Wei Sarath Chandar Soroush Vosoughi Teruko Mitamura and Eduard Hovy. 2021. A Survey of Data Augmentation Approaches for NLP. In ACL Findings\u201921. 968\u2013988."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Luyu Gao Zhuyun Dai and Jamie Callan. 2021. Rethink Training of BERT Rerankers in Multi-stage Retrieval Pipeline. In ECIR\u201921. 280\u2013286.","DOI":"10.1007\/978-3-030-72240-1_26"},{"key":"e_1_3_2_1_14_1","unstructured":"Tianyu Gao Xingcheng Yao and Danqi Chen. 2021. SimCSE: Simple Contrastive Learning of Sentence Embeddings. In EMNLP\u201921. 6894\u20136910."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"John Giorgi Osvald Nitski Bo Wang and Gary Bader. 2021. DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. In ACL\u201921. 879\u2013895.","DOI":"10.18653\/v1\/2021.acl-long.72"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Nilesh Gupta Sakina Bohra Yashoteja Prabhu Saurabh Purohit and Manik Varma. 2021. Generalized Zero-Shot Extreme Multi-label Learning. In KDD\u201921. 527\u2013535.","DOI":"10.1145\/3447548.3467426"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290979"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Himanshu Jain Yashoteja Prabhu and Manik Varma. 2016. Extreme multi-label loss functions for recommendation tagging ranking & other missing label applications. In KDD\u201916. 935\u2013944.","DOI":"10.1145\/2939672.2939756"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00263"},{"key":"e_1_3_2_1_20_1","unstructured":"Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML\u201914. 1188\u20131196."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"e_1_3_2_1_22_1","unstructured":"Jingzhou Liu Wei-Cheng Chang Yuexin Wu and Yiming Yang. 2017. Deep learning for extreme multi-label text classification. In SIGIR\u201917. 115\u2013124."},{"key":"e_1_3_2_1_23_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692(2019).","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692(2019)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Zhiyong Lu. 2011. PubMed and beyond: a survey of web tools for searching biomedical literature. Database 2011(2011).","DOI":"10.1093\/database\/baq036"},{"key":"e_1_3_2_1_25_1","unstructured":"Dongsheng Luo Wei Cheng Jingchao Ni Wenchao Yu Xuchao Zhang Bo Zong Yanchi Liu Zhengzhang Chen Dongjin Song Haifeng Chen 2021. Unsupervised Document Embedding via Contrastive Augmentation. arXiv preprint arXiv:2103.14542(2021)."},{"key":"e_1_3_2_1_26_1","volume-title":"Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. NeurIPS\u201919","author":"Kumar\u00a0Reddy Medini Tharun","year":"2019","unstructured":"Tharun Kumar\u00a0Reddy Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, and Anshumali Shrivastava. 2019. Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. NeurIPS\u201919 (2019), 13265\u201313275."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Dheeraj Mekala and Jingbo Shang. 2020. Contextualized Weak Supervision for Text Classification.. In ACL\u201920. 323\u2013333.","DOI":"10.18653\/v1\/2020.acl-main.30"},{"key":"e_1_3_2_1_28_1","volume-title":"META: Metadata-Empowered Weak Supervision for Text Classification. In EMNLP\u201920. 8351\u20138361.","author":"Mekala Dheeraj","year":"2020","unstructured":"Dheeraj Mekala, Xinyang Zhang, and Jingbo Shang. 2020. META: Metadata-Empowered Weak Supervision for Text Classification. In EMNLP\u201920. 8351\u20138361."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Yu Meng Jiaming Shen Chao Zhang and Jiawei Han. 2018. Weakly-supervised neural text classification. In CIKM\u201918. 983\u2013992.","DOI":"10.1145\/3269206.3271737"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Yu Meng Yunyi Zhang Jiaxin Huang Chenyan Xiong Heng Ji Chao Zhang and Jiawei Han. 2020. Weakly-Supervised Text Classification Using Label Names Only: A Language Model Self-Training Approach. In EMNLP\u201920. 9006\u20139017.","DOI":"10.18653\/v1\/2020.emnlp-main.724"},{"key":"e_1_3_2_1_31_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg\u00a0S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS\u201913. 3111\u20133119."},{"key":"e_1_3_2_1_32_1","volume-title":"ECLARE: Extreme Classification with Label Graph Correlations. In WWW\u201921. 3721\u20133732.","author":"Mittal Anshul","year":"2021","unstructured":"Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar, and Manik Varma. 2021. ECLARE: Extreme Classification with Label Graph Correlations. In WWW\u201921. 3721\u20133732."},{"key":"e_1_3_2_1_33_1","unstructured":"Jinseok Nam Eneldo\u00a0Loza Menc\u00eda and Johannes F\u00fcrnkranz. 2016. All-in text: Learning document label and word representations jointly. In AAAI\u201916. 1948\u20131954."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Jinseok Nam Eneldo\u00a0Loza Menc\u00eda Hyunwoo\u00a0J Kim and Johannes F\u00fcrnkranz. 2015. Predicting unseen labels using label hierarchies in large-scale multi-label learning. In ECML-PKDD\u201915. 102\u2013118.","DOI":"10.1007\/978-3-319-23528-8_7"},{"key":"e_1_3_2_1_35_1","unstructured":"Rodrigo Nogueira Wei Yang Kyunghyun Cho and Jimmy Lin. 2019. Multi-stage document ranking with bert. arXiv preprint arXiv:1910.14424(2019)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185998"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In EMNLP\u201919.","DOI":"10.18653\/v1\/D19-1410"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Anthony Rios and Ramakanth Kavuluru. 2018. Few-shot and zero-shot multi-label learning for structured label spaces. In EMNLP\u201918 Vol.\u00a02018. 3132.","DOI":"10.18653\/v1\/D18-1352"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Stephen\u00a0E Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR\u201994. 232\u2013241.","DOI":"10.1007\/978-1-4471-2099-5_24"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Deepak Saini Arnav\u00a0Kumar Jain Kushal Dave Jian Jiao Amit Singh Ruofei Zhang and Manik Varma. 2021. GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification. In WWW\u201921. 3733\u20133744.","DOI":"10.1145\/3442381.3449937"},{"key":"e_1_3_2_1_41_1","unstructured":"Axel Schulz Aristotelis Hadjakos Heiko Paulheim Johannes Nachtwey and Max M\u00fchlh\u00e4user. 2013. A multi-indicator approach for geolocalization of tweets. In ICWSM\u201913."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Jiaming Shen Wenda Qiu Yu Meng Jingbo Shang Xiang Ren and Jiawei Han. 2021. TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names. In NAACL-HLT\u201921. 4239\u20134249.","DOI":"10.18653\/v1\/2021.naacl-main.335"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Zhihong Shen Hao Ma and Kuansan Wang. 2018. A Web-scale system for scientific knowledge exploration. In ACL\u201918 System Demonstrations. 87\u201392.","DOI":"10.18653\/v1\/P18-4015"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Yangqiu Song and Dan Roth. 2014. On dataless hierarchical text classification. In AAAI\u201914. 1579\u20131585.","DOI":"10.1609\/aaai.v28i1.8938"},{"key":"e_1_3_2_1_45_1","unstructured":"Yizhou Sun Rick Barber Manish Gupta Charu\u00a0C Aggarwal and Jiawei Han. 2011. Co-author relationship prediction in heterogeneous bibliographic networks. In ASONAM\u201911. 121\u2013128."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.5555\/2371211"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_1_48_1","unstructured":"Duyu Tang Bing Qin and Ting Liu. 2015. Learning semantic representations of users and products for document level sentiment classification. In ACL\u201915. 1014\u20131023."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1162\/qss_a_00021"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3324926"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Houye Ji Chuan Shi Bai Wang Yanfang Ye Peng Cui and Philip\u00a0S Yu. 2019. Heterogeneous graph attention network. In WWW\u201919. 2022\u20132032.","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Zihan Wang Dheeraj Mekala and Jingbo Shang. 2020. X-Class: Text Classification with Extremely Weak Supervision. arXiv preprint arXiv:2010.12794(2020).","DOI":"10.18653\/v1\/2021.naacl-main.242"},{"key":"e_1_3_2_1_53_1","volume-title":"EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. In EMNLP\u201919. 6382\u20136388.","author":"Wei Jason","year":"2019","unstructured":"Jason Wei and Kai Zou. 2019. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. In EMNLP\u201919. 6382\u20136388."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"crossref","unstructured":"Tong Wei and Yu-Feng Li. 2018. Does tail label help for large-scale multi-label learning. In IJCAI\u201918. 2847\u20132853.","DOI":"10.24963\/ijcai.2018\/395"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"crossref","unstructured":"Tong Wei Wei-Wei Tu Yu-Feng Li and Guo-Ping Yang. 2021. Towards Robust Prediction on Tail Labels. In KDD\u201921. 1812\u20131820.","DOI":"10.1145\/3447548.3467223"},{"key":"e_1_3_2_1_56_1","volume-title":"Clear: Contrastive learning for sentence representation. arXiv preprint arXiv:2012.15466(2020).","author":"Wu Zhuofeng","year":"2020","unstructured":"Zhuofeng Wu, Sinong Wang, Jiatao Gu, Madian Khabsa, Fei Sun, and Hao Ma. 2020. Clear: Contrastive learning for sentence representation. arXiv preprint arXiv:2012.15466(2020)."},{"key":"e_1_3_2_1_57_1","volume-title":"Unsupervised Data Augmentation for Consistency Training. NeurIPS\u201920","author":"Xie Qizhe","year":"2020","unstructured":"Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. 2020. Unsupervised Data Augmentation for Consistency Training. NeurIPS\u201920 (2020)."},{"key":"e_1_3_2_1_58_1","unstructured":"Yuanmeng Yan Rumei Li Sirui Wang Fuzheng Zhang Wei Wu and Weiran Xu. 2021. ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer. In ACL\u201921. 5065\u20135075."},{"key":"e_1_3_2_1_59_1","volume-title":"Heterogeneous network representation learning: A unified framework with survey and benchmark","author":"Yang Carl","year":"2020","unstructured":"Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, and Jiawei Han. 2020. Heterogeneous network representation learning: A unified framework with survey and benchmark. IEEE TKDE (2020)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098083"},{"key":"e_1_3_2_1_61_1","unstructured":"Wenpeng Yin Jamaal Hay and Dan Roth. 2019. Benchmarking Zero-shot Text Classification: Datasets Evaluation and Entailment Approach. In EMNLP\u201919. 3905\u20133914."},{"key":"e_1_3_2_1_62_1","volume-title":"Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification. NeurIPS\u201919","author":"You Ronghui","year":"2019","unstructured":"Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, and Shanfeng Zhu. 2019. Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification. NeurIPS\u201919 (2019), 5820\u20135830."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"crossref","unstructured":"Daokun Zhang Jie Yin Xingquan Zhu and Chengqi Zhang. 2018. Metagraph2vec: Complex semantic path augmented heterogeneous network embedding. In PAKDD\u201918. 196\u2013208.","DOI":"10.1007\/978-3-319-93037-4_16"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"crossref","unstructured":"Xinyang Zhang Chenwei Zhang Xin\u00a0Luna Dong Jingbo Shang and Jiawei Han. 2021. Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks. In WWW\u201921. 3258\u20133268.","DOI":"10.1145\/3442381.3450114"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"crossref","unstructured":"Yu Zhang Xiusi Chen Yu Meng and Jiawei Han. 2021. Hierarchical Metadata-Aware Document Categorization under Weak Supervision. In WSDM\u201921. 770\u2013778.","DOI":"10.1145\/3437963.3441730"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"crossref","unstructured":"Yu Zhang Shweta Garg Yu Meng Xiusi Chen and Jiawei Han. 2021. MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information. arXiv preprint arXiv:2111.04022(2021).","DOI":"10.1145\/3488560.3498384"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"crossref","unstructured":"Yu Zhang Yu Meng Jiaxin Huang Frank\u00a0F. Xu Xuan Wang and Jiawei Han. 2020. Minimally Supervised Categorization of Text with Metadata. In SIGIR\u201920. 1231\u20131240.","DOI":"10.1145\/3397271.3401168"},{"key":"e_1_3_2_1_68_1","volume-title":"MATCH: Metadata-Aware Text Classification in A Large Hierarchy. In WWW\u201921. 3246\u20133257.","author":"Zhang Yu","year":"2021","unstructured":"Yu Zhang, Zhihong Shen, Yuxiao Dong, Kuansan Wang, and Jiawei Han. 2021. MATCH: Metadata-Aware Text Classification in A Large Hierarchy. In WWW\u201921. 3246\u20133257."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3133067"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"crossref","unstructured":"Yu Zhang Frank\u00a0F. Xu Sha Li Yu Meng Xuan Wang Qi Li and Jiawei Han. 2019. HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories. In ICDM\u201919. 876\u2013885.","DOI":"10.1109\/ICDM.2019.00098"}],"event":{"name":"WWW '22: The ACM Web Conference 2022","location":"Virtual Event, Lyon France","acronym":"WWW '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512174","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512174","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512174","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:14Z","timestamp":1750188674000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":70,"alternative-id":["10.1145\/3485447.3512174","10.1145\/3485447"],"URL":"https:\/\/doi.org\/10.1145\/3485447.3512174","relation":{},"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"2022-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}