Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Corpus-based statistical sense resolution
HLT '93 Proceedings of the workshop on Human Language Technology
Improved Unsupervised Name Discrimination with Very Wide Bigrams and Automatic Cluster Stopping
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Unsupervised Discrimination of Person Names in Web Contexts
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Semantic density analysis: comparing word meaning across time and phonetic space
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
The effect of different context representations on word sense discrimination in biomedical texts
Proceedings of the 1st ACM International Health Informatics Symposium
Is singular value decomposition useful for word similarity extraction?
Language Resources and Evaluation
Discovering text patterns by a new graphic model
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Developing an algorithm for mining semantics in texts
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
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The goal of the on-going project described in this paper is evaluation of the utility of Latent Semantic Analysis (LSA) for unsupervised word sense discrimination. The hypothesis is that LSA can be used to compute context vectors for ambiguous words that can be clustered together --- with each cluster corresponding to a different sense of the word. In this paper we report first experimental result on tightness, separation and purity of sense-based clusters as a function of vector space dimensionality and using different distance metrics.