Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
Blogosonomy: Autotagging Any Text Using Bloggers' Knowledge
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Tag recommendations in social bookmarking systems
AI Communications
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Hashtag retrieval in a microblogging environment
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Comparing twitter and traditional media using topic models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Topical keyphrase extraction from Twitter
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Phrase-based translation model for question retrieval in community question answer archives
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
Proceedings of the 20th ACM international conference on Information and knowledge management
A simple word trigger method for social tag suggestion
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Short text classification improved by learning multi-granularity topics
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Finding bursty topics from microblogs
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Hashtags can be viewed as an indication to the context of the tweet or as the core idea expressed in the tweet. They provide valuable information for many applications, such as information retrieval, opinion mining, text classification, and so on. However, only a small number of microblogs are manually tagged. To address this problem, in this work, we propose a topical translation model for microblog hashtag suggestion. We assume that the content and hashtags of the tweet are talking about the same themes but written in different languages. Under the assumption, hashtag suggestion is modeled as a translation process from content to hashtags. Moreover, in order to cover the topic of tweets, the proposed model regards the translation probability to be topic-specific. It uses topic-specific word trigger to bridge the vocabulary gap between the words in tweets and hashtags, and discovers the topics of tweets by a topic model designed for microblogs. Experimental results on the dataset crawled from real world microblogging service demonstrate that the proposed method outperforms state-of-the-art methods.