Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A sequential pruning strategy for the selection of the number of states in hidden Markov models
Pattern Recognition Letters
The Journal of Machine Learning Research
Simple Semantics in Topic Detection and Tracking
Information Retrieval
Tracking dynamics of topic trends using a finite mixture model
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
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A mixture model for contextual text mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semantic multi-grain mixture topic model for text analysis
Expert Systems with Applications: An International Journal
Trend-based and reputation-versed personalized news network
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Topics modeling based on selective Zipf distribution
Expert Systems with Applications: An International Journal
A hybrid generative/discriminative method for semi-supervised classification
Knowledge-Based Systems
| Hi-index | 12.05 |
Topics often transit among documents in a document collection. To improve the accuracy of the topic detection and tracking (TDT) algorithms in discovering topics or classifying documents, it is necessary to make full use of this kind of topic transition information. However, TDT algorithms usually find topics based on topic models, such as LDA, pLSI, etc., which are a kind of mixture model and make the topic transition difficult to be denoted and implemented. A topic transition model representation based on hidden Markov model is present, and learning the topic transition from documents is discussed. Based on the model, two TDT algorithms incorporating topic transition, i.e. topic discovering and document classifying, are provided to show the application of the proposed model. Experiments on two real-world document collections are done with the two algorithms, and performance comparison with other similar algorithm shows that the accuracy can achieve 93% for topic discovering in Reuters-21578, and 97.3% in document classifying. Furthermore, topic transition discovered by the algorithm on a dataset which was collected from a BBS website is consistent with the manual analysis results.