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In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.<\/jats:p>","DOI":"10.1145\/3332932","type":"journal-article","created":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T12:50:33Z","timestamp":1562071833000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Time-Sync Video Tag Extraction Using Semantic Association Graph"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8493-4449","authenticated-orcid":false,"given":"Wenmain","family":"Yang","sequence":"first","affiliation":[{"name":"Shanghai JiaoTong University, University of Macau, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Ruan","sequence":"additional","affiliation":[{"name":"Shanghai JiaoTong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyuan","family":"Gao","sequence":"additional","affiliation":[{"name":"Shanghai JiaoTong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijia","family":"Jia","sequence":"additional","affiliation":[{"name":"University of Macau, Shanghai JiaoTong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"American University of Sharjah, Sharjah, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Liu","sequence":"additional","affiliation":[{"name":"China Unicom Research Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Unicom Research Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,7,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.12.040"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/795666.796605"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2992785"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/361002.361007"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/98524.98564"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835449.1835536"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2953883"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123420"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080776"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/NLPKE.2003.1276017"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/355744.355745"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2017.7996546"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063619"},{"volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP\u201915)","author":"He Hua","key":"e_1_2_1_16_1","unstructured":"Hua He , Kevin Gimpel , and Jimmy J. 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