{"type":"ScholarlyArticle","title":"Community Question Answering Entity Linking via Leveraging Auxiliary Data","authors":[{"name":"Yuhan Li","slug":"yuhan-li","given":"Yuhan","family":"Li","affiliation":"TMCC , TKLNDST , College of Computer Science , Nankai University , Tianjin , China"},{"name":"Wei Shen","slug":"wei-shen","given":"Wei","family":"Shen","affiliation":"TMCC , TKLNDST , College of Computer Science , Nankai University , Tianjin , China"},{"name":"Jianbo Gao","slug":"jianbo-gao","given":"Jianbo","family":"Gao","affiliation":"TMCC , TKLNDST , College of Computer Science , Nankai University , Tianjin , China"},{"name":"Yadong Wang","slug":"yadong-wang","given":"Yadong","family":"Wang","affiliation":"TMCC , TKLNDST , College of Computer Science , Nankai University , Tianjin , China"}],"abstract":"Community Question Answering (CQA) platforms contain plenty of CQA texts (i.e., questions and answers corresponding to the question) where named entities appear ubiquitously. In this paper, we define a new task of CQA entity linking (CQAEL) as linking the textual entity mentions detected from CQA texts with their corresponding entities in a knowledge base. This task can facilitate many downstream applications including expert finding and knowledge base enrichment. Traditional entity linking methods mainly focus on linking entities in news documents, and are suboptimal over this new task of CQAEL since they cannot effectively leverage various informative auxiliary data involved in the CQA platform to aid entity linking, such as parallel answers and two types of meta-data (i.e., topic tags and users). To remedy this crucial issue, we propose a novel transformer-based framework to effectively harness the knowledge delivered by different kinds of auxiliary data to promote the linking performance. We validate the superiority of our framework through extensive experiments over a newly released CQAEL data set against state-of-the-art entity linking methods.","doi":"10.24963/ijcai.2022/298","url":"https://ijcai.org/proceedings/2022/298","pdf":"https://ijcai.org/proceedings/2022/0298.pdf","markdown":"https://ijcai.org/proceedings/2022/298.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/298","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":298,"pages":{"first":2145,"last":2151},"keywords":["Data Mining: Mining Text, Web, Social Media"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986900","length_sec":87,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985043","length_sec":337,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Beltagy et al., 2020] Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020.","title":"Longformer: The long-document transformer","authors":"Beltagy","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bhagavatula et al., 2015] Chandra Sekhar Bhagavatula, Thanapon Noraset, and Doug Downey. Tabel: Entity linking in web tables. In ISWC, pages 425-441, 2015.","title":"Tabel: Entity linking in web tables","authors":"Chandra Sekhar Bhagavatula, Thanapon Noraset, Doug Downey","year":"2015","venue":"ISWC","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2020] Ermei Cao, Difeng Wang, Jiacheng Huang, and Wei Hu. Open knowledge enrichment for long-tail entities. In WWW, pages 384-394, 2020.","title":"Open knowledge enrichment for long-tail entities","authors":"Cao","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dai et al., 2019] Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G Carbonell, Quoc Le, and Ruslan Salakhutdinov. Transformer- xl: Attentive language models beyond a fixed-length context. In ACL, pages 2978-2988, 2019.","title":"Transformerxl: Attentive language models beyond a fixed-length context","authors":"Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G Carbonell, Quoc Le, Ruslan Salakhutdinov","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[De Cao et al., 2021] Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. Autoregressive entity retrieval. In ICLR, 2021.","title":"Autoregressive entity retrieval","authors":"Nicola De Cao, Gautier Izacard, Sebastian Riedel, Fabio Petroni","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[DeYoung et al., 2021] Jay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl, and Lucy Wang. MSˆ2: Multi-document summarization of medical studies. In EMNLP, pages 7494-7513, 2021.","title":"MSˆ2: Multi-document summarization of medical studies","authors":"Jay Deyoung, Iz Beltagy, Madeleine Van Zuylen, Bailey Kuehl, Lucy Wang","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Fang et al., 2019] Zheng Fang, Yanan Cao, Qian Li, Dongjie Zhang, Zhenyu Zhang, and Yanbing Liu. Joint entity linking with deep reinforcement learning. In WWW, pages 438-447, 2019.","title":"Joint entity linking with deep reinforcement learning","authors":"Zheng Fang, Yanan Cao, Qian Li, Dongjie Zhang, Zhenyu Zhang, Yanbing Liu","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fang et al., 2020] Zheng Fang, Yanan Cao, Ren Li, Zhenyu Zhang, Yanbing Liu, and Shi Wang. High quality candidate genera- tion and sequential graph attention network for entity linking. In WWW, pages 640-650, 2020.","title":"High quality candidate generation and sequential graph attention network for entity linking","authors":"Zheng Fang, Yanan Cao, Ren Li, Zhenyu Zhang, Yanbing Liu, Shi Wang","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gan et al., 2021] Jingru Gan, Jinchang Luo, Haiwei Wang, Shuhui Wang, Wei He, and Qingming Huang. Multimodal entity linking: A new dataset and a baseline. In MM, pages 993-1001, 2021.","title":"Multimodal entity linking: A new dataset and a baseline","authors":"Gan","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ganea and Hofmann, 2017] Octavian-Eugen Ganea and Thomas Hofmann. Deep joint entity disambiguation with local neural at- tention. In EMNLP, pages 2619-2629, 2017.","title":"Deep joint entity disambiguation with local neural attention","authors":"Hofmann ; -Eugen Ganea, Thomas Ganea, Hofmann","year":"2017","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hou et al., 2020] Feng Hou, Ruili Wang, Jun He, and Yi Zhou. Im- proving entity linking through semantic reinforced entity embed- dings. In ACL, pages 6843-6848, 2020.","title":"Improving entity linking through semantic reinforced entity embeddings","authors":"Feng Hou, Ruili Wang, Jun He, Yi Zhou","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Le and Titov, 2018] Phong Le and Ivan Titov. Improving entity linking by modeling latent relations between mentions. In ACL, pages 1595-1604, 2018.","title":"Improving entity linking by modeling latent relations between mentions","authors":"Le, Titov ;, Phong Le, Ivan Titov","year":"2018","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lewis et al., 2020] Mike Lewis, Yinhan Liu, Naman Goyal, Mar- jan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. Bart: Denoising sequence- to-sequence pre-training for natural language generation, trans- lation, and comprehension. In ACL, pages 7871-7880, 2020.","title":"Bart: Denoising sequenceto-sequence pre-training for natural language generation, translation, and comprehension","authors":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, Luke Zettlemoyer","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2021] Nuo Li, Bin Guo, Yan Liu, Lina Yao, Jiaqi Liu, and Zhiwen Yu. Askme: joint individual-level and community-level behavior interaction for question recommendation. WWW, pages 1-24, 2021.","title":"Askme: joint individual-level and community-level behavior interaction for question recommendation","authors":"Nuo Li, Bin Guo, Yan Liu, Lina Yao, Jiaqi Liu, Zhiwen Yu","year":"2021","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2021] Yinan Liu, Wei Shen, Yuanfei Wang, Jianyong Wang, Zhenglu Yang, and Xiaojie Yuan. Joint open knowledge base canonicalization and linking. In SIGMOD, pages 2253- 2261, 2021.","title":"Joint open knowledge base canonicalization and linking","authors":"Yinan Liu, Wei Shen, Yuanfei Wang, Jianyong Wang, Zhenglu Yang, Xiaojie Yuan","year":"2021","venue":"SIGMOD","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Logeswaran et al., 2019] Lajanugen Logeswaran, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, Jacob Devlin, and Honglak Lee. Zero-shot entity linking by reading entity descriptions. In ACL, pages 3449-3460, 2019.","title":"Zero-shot entity linking by reading entity descriptions","authors":"Logeswaran","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Loshchilov and Hutter, 2018] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2018. Lyu et al., 2019]","title":"Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization","authors":"Hutter Loshchilov","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"Shanshan Lyu, Wentao Ouyang, Yongqing Wang, Huawei Shen, and Xueqi Cheng. What we vote for? answer se- lection from user expertise view in community question answer- ing. In WWW, pages 1198-1209, 2019.","title":"What we vote for? answer selection from user expertise view in community question answering","authors":"Shanshan Lyu, Wentao Ouyang, Yongqing Wang, Huawei Shen, Xueqi Cheng","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Phan et al., 2017] Minh C Phan, Aixin Sun, Yi Tay, Jialong Han, and Chenliang Li. Neupl: Attention-based semantic match- ing and pair-linking for entity disambiguation. In CIKM, pages 1667-1676, 2017.","title":"Neupl: Attention-based semantic matching and pair-linking for entity disambiguation","authors":"Aixin Minh C Phan, Yi Sun, Jialong Tay, Chenliang Han, Li","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ran et al., 2018] Chenwei Ran, Wei Shen, and Jianyong Wang. An attention factor graph model for tweet entity linking. In WWW, pages 1135-1144, 2018.","title":"An attention factor graph model for tweet entity linking","authors":"Ran","year":"2018","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Ratinov et al., 2011] Lev Ratinov, Dan Roth, Doug Downey, and Mike Anderson. Local and global algorithms for disambiguation to wikipedia. In ACL, pages 1375-1384, 2011.","title":"Local and global algorithms for disambiguation to wikipedia","authors":"Ratinov","year":"2011","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Rücklé et al., 2019] Andreas Rücklé, Krishnkant Swarnkar, and Iryna Gurevych. Improved cross-lingual question retrieval for community question answering. In WWW, pages 3179-3186, 2019.","title":"Iryna Gurevych. Improved cross-lingual question retrieval for community question answering","authors":"Rücklé","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Shen et al., 2014] Wei Shen, Jianyong Wang, and Jiawei Han. En- tity linking with a knowledge base: Issues, techniques, and solu- tions. TKDE, 27(2):443-460, 2014.","title":"Entity linking with a knowledge base: Issues, techniques, and solutions","authors":"Wei Shen, Jianyong Wang, Jiawei Han","year":"2014","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Shen et al., 2021] Wei Shen, Yuhan Li, Yinan Liu, Jiawei Han, Jianyong Wang, and Xiaojie Yuan. Entity linking meets deep learning: Techniques and solutions. TKDE, 2021.","title":"Entity linking meets deep learning: Techniques and solutions","authors":"Wei Shen, Yuhan Li, Yinan Liu, Jiawei Han, Jianyong Wang, Xiaojie Yuan","year":"2021","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Song et al., 2017] Hongya Song, Zhaochun Ren, Shangsong Liang, Piji Li, Jun Ma, and Maarten de Rijke. Summarizing an- swers in non-factoid community question-answering. In WSDM, pages 405-414, 2017.","title":"Summarizing answers in non-factoid community question-answering","authors":"Song","year":"2017","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Tang et al., 2021] Hongyin Tang, Xingwu Sun, Beihong Jin, and Fuzheng Zhang. A bidirectional multi-paragraph reading model for zero-shot entity linking. In AAAI, pages 13889-13897, 2021.","title":"A bidirectional multi-paragraph reading model for zero-shot entity linking","authors":"Tang","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Tomasoni and Huang, 2010] Mattia Tomasoni and Minlie Huang. Metadata-aware measures for answer summarization in commu- nity question answering. In ACL, pages 760-769, 2010.","title":"Metadata-aware measures for answer summarization in community question answering","authors":"Huang Tomasoni, Mattia Tomasoni, Minlie Huang","year":"2010","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[van Hulst et al., 2020] Johannes M van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog, and Arjen P de Vries. Rel: An entity linker standing on the shoulders of giants. In SIGIR, pages 2197-2200, 2020.","title":"Rel: An entity linker standing on the shoulders of giants","authors":"Van Hulst","year":"2020","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2017] Fang Wang, Wei Wu, Zhoujun Li, and Ming Zhou. Named entity disambiguation for questions in community question answering. KBS, 126:68-77, 2017.","title":"Named entity disambiguation for questions in community question answering","authors":"Fang Wang, Wei Wu, Zhoujun Li, Ming Zhou","year":"2017","venue":"KBS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wu et al., 2020] Ledell Wu, Fabio Petroni, Martin Josifoski, Se- bastian Riedel, and Luke Zettlemoyer. Scalable zero-shot entity linking with dense entity retrieval. In EMNLP, pages 6397-6407, 2020.","title":"Scalable zero-shot entity linking with dense entity retrieval","authors":"Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, Luke Zettlemoyer","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Xue et al., 2019] Mengge Xue, Weiming Cai, Jinsong Su, Linfeng Song, Yubin Ge, Yubao Liu, and Bin Wang. Neural collective entity linking based on recurrent random walk network learning. In IJCAI, pages 5327-5333, 2019.","title":"Neural collective entity linking based on recurrent random walk network learning","authors":"Mengge Xue, Weiming Cai, Jinsong Su, Linfeng Song, Yubin Ge, Yubao Liu, Bin Wang","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/740"},{"position":32,"raw":"[Yang et al., 2019] Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. Xlnet: Gen- eralized autoregressive pretraining for language understanding. NeurIPS, 32:5753-5763, 2019.","title":"Xlnet: Generalized autoregressive pretraining for language understanding","authors":"Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, Quoc V Le","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zhou et al., 2015] Guangyou Zhou, Tingting He, Jun Zhao, and Po Hu. Learning continuous word embedding with metadata for question retrieval in community question answering. In ACL, pages 250-259, 2015.","title":"Learning continuous word embedding with metadata for question retrieval in community question answering","authors":"Zhou","year":"2015","venue":"ACL","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}