@inproceedings{tran2015semantic,
  abstract = {In this paper we study the problem of semantic annotation for a trending hashtag which is the crucial step towards analyzing user behavior in social media, yet has been largely unexplored. We tackle the problem via linking to entities from Wikipedia. We incorporate the social aspects of trending hashtags by identifying prominent entities for the annotation so as to maximize the information spreading in entity networks. We exploit temporal dynamics of entities in Wikipedia, namely Wikipedia edits and page views to improve the annotation quality. Our experiments show that we significantly outperform the established methods in tweet annotation.},
  added-at = {2015-10-16T11:08:54.000+0200},
  author = {Tran, Tuan and Tran, Nam-Khanh and Teka Hadgu, Asmelash and Jäschke, Robert},
  biburl = {https://www.bibsonomy.org/bibtex/29d4cd9070922e1eb43bcab1da4a9d840/kde-alumni},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  interhash = {4156275c801376fa64dfdb69a4ce60c4},
  intrahash = {9d4cd9070922e1eb43bcab1da4a9d840},
  keywords = {2015 annotation microblogging myown semantics temporal topic twitter wikipedia},
  month = sep,
  publisher = {Association for Computational Linguistics},
  timestamp = {2015-10-16T11:08:54.000+0200},
  title = {Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information},
  year = 2015
}

@inproceedings{tran2015semantic,
  abstract = {In this paper we study the problem of semantic annotation for a trending hashtag which is the crucial step towards analyzing user behavior in social media, yet has been largely unexplored. We tackle the problem via linking to entities from Wikipedia. We incorporate the social aspects of trending hashtags by identifying prominent entities for the annotation so as to maximize the information spreading in entity networks. We exploit temporal dynamics of entities in Wikipedia, namely Wikipedia edits and page views to improve the annotation quality. Our experiments show that we significantly outperform the established methods in tweet annotation.},
  added-at = {2015-08-04T14:37:24.000+0200},
  author = {Tran, Tuan and Tran, Nam-Khanh and Teka Hadgu, Asmelash and Jäschke, Robert},
  biburl = {https://www.bibsonomy.org/bibtex/29d4cd9070922e1eb43bcab1da4a9d840/hotho},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  interhash = {4156275c801376fa64dfdb69a4ce60c4},
  intrahash = {9d4cd9070922e1eb43bcab1da4a9d840},
  keywords = {microblogging toread web},
  month = sep,
  publisher = {Association for Computational Linguistics},
  timestamp = {2015-08-04T14:37:24.000+0200},
  title = {Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information},
  year = 2015
}

@inproceedings{tran2015semantic,
  abstract = {Trending topics in microblogs such as Twitter are valuable resources
  to understand social aspects of real-world events. To enable deep
  analyses of such trends, semantic annotation is an effective
  approach; yet the problem of annotating microblog trending topics is
  largely unexplored by the research community. In this work, we
  tackle the problem of mapping trending Twitter topics to entities
  from Wikipedia. We propose a novel model that complements
  traditional text-based approaches by rewarding entities that exhibit
  a high temporal correlation with topics during their burst time
  period. By exploiting temporal information from the Wikipedia edit
  history and page view logs, we have improved the annotation
  performance by 17-28%, as compared to the competitive baselines.},
  added-at = {2015-07-31T16:16:58.000+0200},
  author = {Tran, Tuan and Tran, Nam-Khanh and Teka Hadgu, Asmelash and Jäschke, Robert},
  biburl = {https://www.bibsonomy.org/bibtex/29d4cd9070922e1eb43bcab1da4a9d840/antoine-tran},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  interhash = {4156275c801376fa64dfdb69a4ce60c4},
  intrahash = {9d4cd9070922e1eb43bcab1da4a9d840},
  keywords = {alexandria annotation forgetit gutearbeit l3s l3s_twitter microblog myown qualimaster semantic wikipedia},
  month = sep,
  publisher = {Association for Computational Linguistics},
  timestamp = {2017-01-14T17:55:08.000+0100},
  title = {Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information},
  year = 2015
}

@inproceedings{tran2015semantic,
  abstract = {In this paper we study the problem of semantic annotation for a trending hashtag which is the crucial step towards analyzing user behavior in social media, yet has been largely unexplored. We tackle the problem via linking to entities from Wikipedia. We incorporate the social aspects of trending hashtags by identifying prominent entities for the annotation so as to maximize the information spreading in entity networks. We exploit temporal dynamics of entities in Wikipedia, namely Wikipedia edits and page views to improve the annotation quality. Our experiments show that we significantly outperform the established methods in tweet annotation.},
  added-at = {2015-07-31T08:30:51.000+0200},
  author = {Tran, Tuan and Tran, Nam-Khanh and Hadgu, Asmelash Teka and Jäschke, Robert},
  biburl = {https://www.bibsonomy.org/bibtex/29d4cd9070922e1eb43bcab1da4a9d840/jaeschke},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  doi = {10.18653/v1/D15-1010},
  interhash = {4156275c801376fa64dfdb69a4ce60c4},
  intrahash = {9d4cd9070922e1eb43bcab1da4a9d840},
  keywords = {2015 annotation microblogging myown semantics temporal topic twitter wikipedia},
  month = sep,
  pages = {97--106},
  publisher = {Association for Computational Linguistics},
  timestamp = {2023-07-06T17:09:59.000+0200},
  title = {Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information},
  url = {https://aclanthology.org/D15-1010/},
  year = 2015
}