WordNet: a lexical database for English
Communications of the ACM
A systematic comparison of various statistical alignment models
Computational Linguistics
The Journal of Machine Learning Research
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
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Collocation extraction using monolingual word alignment method
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Modern Information Retrieval
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Automatic keyphrase extraction via topic decomposition
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Recommender Systems Handbook
A simple word trigger method for social tag suggestion
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Content-based social tagging recommendation, which considers the relationship between the tags and the descriptions contained in resources, is proposed to remedy the cold-start problem of collaborative filtering. There is such a common phenomenon that certain tag does not appear in the corresponding description, however, they do semantically relate with each other. State-of-the-art methods seldom consider this phenomenon and thus still need to be improved. In this paper, we propose a novel content-based social tag ranking scheme, aiming to recommend the semantic tags that the descriptions may not contain. The scheme firstly acquires the quantized semantic relationships between words with empirical methods, then constructs the weighted tag-digraph based on the descriptions and acquired quantized semantics, and finally performs a modified graph-based ranking algorithm to refine the score of each candidate tag for recommendation. Experimental results on both English and Chinese datasets show that the proposed scheme performs better than several state-of-the-art content-based methods.