An Introduction to Variational Methods for Graphical Models
Machine Learning
The Journal of Machine Learning Research
Event threading within news topics
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Generating templates of entity summaries with an entity-aspect model and pattern mining
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
An unsupervised topic segmentation model incorporating word order
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Automatic thread extraction for news events can help people know different aspects of a news event. In this paper, we present a method of extraction using a topical N-gram model with a background distribution (TNB). Unlike most topic models, such as Latent Dirichlet Allocation (LDA), which relies on the bag-of-words assumption, our model treats words in their textual order. Each news report is represented as a combination of a background distribution over the corpus and a mixture distribution over hidden news threads. Thus our model can model “presidential election” of different years as a background phrase and “Obama wins” as a thread for event “2008 USA presidential election”. We apply our method on two different corpora. Evaluation based on human judgment shows that the model can generate meaningful and interpretable threads from a news corpus.