Foundations of statistical natural language processing
Foundations of statistical natural language processing
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
An equivalent pseudoword solution to Chinese word sense disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Unsupervised acquisition of predominant word senses
Computational Linguistics
Tag Meaning Disambiguation through Analysis of Tripartite Structure of Folksonomies
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
SemEval-2007 task 07: coarse-grained English all-words task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
NUS-PT: exploiting parallel texts for word sense disambiguation in the English all-words tasks
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
How tagging pragmatics influence tag sense discovery in social annotation systems
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Blog users enjoy tagging for better document organization, while ambiguity in tags leads to inaccuracy in tag-based applications, such as retrieval, visualization or trend discovery. The dynamic nature of tag meanings makes current word sense disambiguation(WSD) methods not applicable. In this paper, we propose an unsupervised method for disambiguating tags in blogs. We first cluster the tags by their context words using Spectral Clustering. Then we compare a tag with these clusters to find the most suitable meaning. We use Normalized Google Distance to measure word similarity, which can be computed by querying search engines, thus reflects the up-to-date meaning of words. No human labeling efforts or dictionary needed in our method. Evaluation using crawled blog data showed a promising micro average precision of 0.842.