Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information
T. Tran, N. Tran, A. Teka Hadgu, и R. Jäschke.
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (Сентябрь 2015)

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.

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@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://puma.uni-kassel.de/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
}

%0 Conference Paper
%1 tran2015semantic
%A Tran, Tuan
%A Tran, Nam-Khanh
%A Teka Hadgu, Asmelash
%A Jäschke, Robert
%B Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2015
%I Association for Computational Linguistics
%K microblogging toread web
%T Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information
%X 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.

PUMA

Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information
T. Tran, N. Tran, A. Teka Hadgu, и R. Jäschke.
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (Сентябрь 2015)

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Semantic Annotation for Microblog Topics Using Wikipedia Temporal InformationT. Tran, N. Tran, A. Teka Hadgu, и R. Jäschke. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (Сентябрь 2015)

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Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information
T. Tran, N. Tran, A. Teka Hadgu, и R. Jäschke.
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (Сентябрь 2015)