An Introduction to Variational Methods for Graphical Models
Machine Learning
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Semantic knowledge in word completion
Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Testing the efficacy of part-of-speech information in word completion
TextEntry '03 Proceedings of the 2003 EACL Workshop on Language Modeling for Text Entry Methods
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We investigate the use of topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), for word completion tasks. The advantage of using these models for such an application is twofold. On the one hand, they allow us to exploit semantic or contextual information when predicting candidate words for completion. On the other hand, these probabilistic models have been found to outperform classical latent semantic analysis (LSA) for modeling text documents. We describe a word completion algorithm that takes into account the semantic context of the word being typed. We also present evaluation metrics to compare different models being used in our study. Our experiments validate our hypothesis of using probabilistic models for semantic analysis of text documents and their application in word completion tasks.