{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:35:16Z","timestamp":1750221316490,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T00:00:00Z","timestamp":1509926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"JSPS KAKENHI","award":["15H01704"],"award-info":[{"award-number":["15H01704"]}]},{"name":"JST ACT-I","award":["JPMJPR16UL"],"award-info":[{"award-number":["JPMJPR16UL"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,11,6]]},"DOI":"10.1145\/3132847.3132971","type":"proceedings-article","created":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T13:30:29Z","timestamp":1509975029000},"page":"1069-1078","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Hyper Questions"],"prefix":"10.1145","author":[{"given":"Jiyi","family":"Li","sequence":"first","affiliation":[{"name":"Kyoto University, Kyoto, Japan"}]},{"given":"Yukino","family":"Baba","sequence":"additional","affiliation":[{"name":"Kyoto University, Kyoto, Japan"}]},{"given":"Hisashi","family":"Kashima","sequence":"additional","affiliation":[{"name":"Kyoto University & RIKEN Center for AIP, Kyoto, Japan"}]}],"member":"320","published-online":{"date-parts":[[2017,11,6]]},"reference":[{"volume-title":"Proceedings of the 29th International Coference on International Conference on Machine Learning (ICML '12)","author":"Bachrach Y.","key":"e_1_3_2_1_1_1","unstructured":"Y. Bachrach , T. Minka , J. Guiver , and T. Graepel . 2012. How to Grade a Test Without Knowing the Answers: A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing . In Proceedings of the 29th International Coference on International Conference on Machine Learning (ICML '12) . 819--826. Y. Bachrach, T. Minka, J. Guiver, and T. Graepel. 2012. How to Grade a Test Without Knowing the Answers: A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing. In Proceedings of the 29th International Coference on International Conference on Machine Learning (ICML '12). 819--826."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"A. P. Dawid and A. M. Skene. 1979. Maximum likelihood estimation of observer error-rates using the EM algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 28 1 (1979).  A. P. Dawid and A. M. Skene. 1979. Maximum likelihood estimation of observer error-rates using the EM algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 28 1 (1979).","DOI":"10.2307\/2346806"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1753326.1753688"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2567988"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1837885.1837906"},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS '11). 1953","author":"Karger D. R.","year":"1961","unstructured":"D. R. Karger , S. Oh , and D. Shah . 2011. Iterative Learning for Reliable Crowd-sourcing Systems . In Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS '11). 1953 -- 1961 . D. R. Karger, S. Oh, and D. Shah. 2011. Iterative Learning for Reliable Crowd-sourcing Systems. In Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS '11). 1953--1961."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2398697"},{"key":"e_1_3_2_1_8_1","unstructured":"H. C. Kim and Z. Ghahramani. 2012. Bayesian classifier combination. In Artificial Intelligence and Statistics (AISTATS '12). 619--627.  H. C. Kim and Z. Ghahramani. 2012. Bayesian classifier combination. In Artificial Intelligence and Statistics (AISTATS '12). 619--627."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2568033"},{"volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS '12)","author":"Liu Q.","key":"e_1_3_2_1_10_1","unstructured":"Q. Liu , J. Peng , and A. Ihler . 2012. Variational Inference for Crowdsourcing . In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS '12) . 692--700. Q. Liu, J. Peng, and A. Ihler. 2012. Variational Inference for Crowdsourcing. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS '12). 692--700."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783314"},{"key":"e_1_3_2_1_12_1","volume-title":"AMIA Annual Symposium Proceedings (AMIA '13)","volume":"2013","author":"Mortensen J. M.","unstructured":"J. M. Mortensen , M. A. Musen , and N. F. Noy . 2013. Crowdsourcing the verification of relationships in biomedical ontologies . In AMIA Annual Symposium Proceedings (AMIA '13) , Vol. 2013 . 1020. J. M. Mortensen, M. A. Musen, and N. F. Noy. 2013. Crowdsourcing the verification of relationships in biomedical ontologies. In AMIA Annual Symposium Proceedings (AMIA '13), Vol. 2013. 1020."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2615569.2615644"},{"volume-title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI '16)","author":"Pan S. Y.","key":"e_1_3_2_1_14_1","unstructured":"S. Y. Pan , K. Larson , J. Bradshaw , and E. Law . 2016. Dynamic Task Allocation Algorithm for Hiring Workers That Learn . In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI '16) . 3825--3831. S. Y. Pan, K. Larson, J. Bradshaw, and E. Law. 2016. Dynamic Task Allocation Algorithm for Hiring Workers That Learn. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI '16). 3825--3831."},{"key":"e_1_3_2_1_15_1","volume-title":"A solution to the single-question crowd wisdom problem. Nature 541, 7638","author":"Prelec D.","year":"2017","unstructured":"D. Prelec , H. S. Seung , and J. McCoy . 2017. A solution to the single-question crowd wisdom problem. Nature 541, 7638 ( 2017 ), 532--535. D. Prelec, H. S. Seung, and J. McCoy. 2017. A solution to the single-question crowd wisdom problem. Nature 541, 7638 (2017), 532--535."},{"key":"e_1_3_2_1_16_1","unstructured":"V. C. Raykar S. P. Yu L. H. Zhao G. H. Valadez C. Florin L. Bogoni and L. Moy. 2010. Learning From Crowds. J. Mach. Learn. Res. 11 (Aug. 2010) 1297--1322.   V. C. Raykar S. P. Yu L. H. Zhao G. H. Valadez C. Florin L. Bogoni and L. Moy. 2010. Learning From Crowds. J. Mach. Learn. Res. 11 (Aug. 2010) 1297--1322."},{"volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '08)","author":"Snow R.","key":"e_1_3_2_1_17_1","unstructured":"R. Snow , B. O'Connor , D. Jurafsky , and A. Y. Ng . 2008. Cheap and Fast--but is It Good?: Evaluating Non-expert Annotations for Natural Language Tasks . In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '08) . 254--263. R. Snow, B. O'Connor, D. Jurafsky, and A. Y. Ng. 2008. Cheap and Fast--but is It Good?: Evaluating Non-expert Annotations for Natural Language Tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '08). 254--263."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74958-5_38"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2567989"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS '11). 1800","author":"Wauthier F. L.","year":"1808","unstructured":"F. L. Wauthier and M. I. Jordan . 2011. Bayesian Bias Mitigation for Crowdsourcing . In Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS '11). 1800 -- 1808 . F. L. Wauthier and M. I. Jordan. 2011. Bayesian Bias Mitigation for Crowdsourcing. In Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS '11). 1800--1808."},{"volume-title":"Proceedings of the 23rd International Conference on Neural Information Processing Systems (NIPS '10)","author":"Welinder P.","key":"e_1_3_2_1_21_1","unstructured":"P. Welinder , S. Branson , S. Belongie , and P. Perona . 2010. The Multidimensional Wisdom of Crowds . In Proceedings of the 23rd International Conference on Neural Information Processing Systems (NIPS '10) . 2424--2432. P. Welinder, S. Branson, S. Belongie, and P. Perona. 2010. The Multidimensional Wisdom of Crowds. In Proceedings of the 23rd International Conference on Neural Information Processing Systems (NIPS '10). 2424--2432."},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS '09). 2035","author":"Whitehill J.","year":"2043","unstructured":"J. Whitehill , P. Ruvolo , T. F. Wu , J. Bergsma , and J. Movellan . 2009. Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise . In Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS '09). 2035 -- 2043 . J. Whitehill, P. Ruvolo, T. F. Wu, J. Bergsma, and J. Movellan. 2009. Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise. In Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS '09). 2035--2043."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.14778\/3055540.3055547"},{"volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS '12)","author":"Zhou D. Y.","key":"e_1_3_2_1_24_1","unstructured":"D. Y. Zhou , J. C. Platt , S. Basu , and Y. Mao . 2012. Learning from the Wisdom of Crowds by Minimax Entropy . In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS '12) . 2195--2203. D. Y. Zhou, J. C. Platt, S. Basu, and Y. Mao. 2012. Learning from the Wisdom of Crowds by Minimax Entropy. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS '12). 2195--2203."}],"event":{"name":"CIKM '17: ACM Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Singapore Singapore","acronym":"CIKM '17"},"container-title":["Proceedings of the 2017 ACM on Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3132847.3132971","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3132847.3132971","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:13:46Z","timestamp":1750212826000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3132847.3132971"}},"subtitle":["Unsupervised Targeting of a Few Experts in Crowdsourcing"],"short-title":[],"issued":{"date-parts":[[2017,11,6]]},"references-count":24,"alternative-id":["10.1145\/3132847.3132971","10.1145\/3132847"],"URL":"https:\/\/doi.org\/10.1145\/3132847.3132971","relation":{},"subject":[],"published":{"date-parts":[[2017,11,6]]},"assertion":[{"value":"2017-11-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}