Topic segmentation with an aspect hidden Markov model
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Statistical entity-topic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised topic modelling for multi-party spoken discourse
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Context-Sensitive Error Correction: Using Topic Models to Improve OCR
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast collapsed gibbs sampling for latent dirichlet allocation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Improving optical character recognition through efficient multiple system alignment
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Bayesian unsupervised topic segmentation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Hierarchical text segmentation from multi-scale lexical cohesion
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Probabilistic models for topic learning from images and captions in online biomedical literatures
Proceedings of the 18th ACM conference on Information and knowledge management
A statistical model for topic segmentation and clustering
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Evaluating models of latent document semantics in the presence of OCR errors
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Towards noise-resilient document modeling
Proceedings of the 20th ACM international conference on Information and knowledge management
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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As large-scale text data become available on the Web, textual errors in a corpus are often inevitable (e.g., digitizing historic documents). Due to the calculation of frequencies of words, however, such textual errors can significantly impact the accuracy of statistical models such as the popular Latent Dirichlet Allocation (LDA) model. To address such an issue, in this paper, we propose two novel extensions to LDA (i.e., TE-LDA and TDE-LDA): (1) The TE-LDA model incorporates textual errors into term generation process; and (2) The TDE-LDA model extends TE-LDA further by taking into account topic dependency to leverage on semantic connections among consecutive words even if parts are typos. Using both real and synthetic data sets with varying degrees of "errors", our TDE-LDA model outperforms: (1) the traditional LDA model by 16%-39% (real) and 20%-63% (synthetic); and (2) the state-of-the-art N-Grams model by 11%-27% (real) and 16%-54% (synthetic).