Extracting significant time varying features from text
Proceedings of the eighth international conference on Information and knowledge management
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Group and topic discovery from relations and text
Proceedings of the 3rd international workshop on Link discovery
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
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
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 and Trend Detection in Text Collections Using Latent Dirichlet Allocation
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trend analysis model: trend consists of temporal words, topics, and timestamps
Proceedings of the fourth ACM international conference on Web search and data mining
The web of topics: discovering the topology of topic evolution in a corpus
Proceedings of the 20th international conference on World wide web
Tracking trends: incorporating term volume into temporal topic models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
LPTA: A Probabilistic Model for Latent Periodic Topic Analysis
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Modeling topical trends over continuous time with priors
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A phrase-discovering topic model using hierarchical Pitman-Yor processes
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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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This paper presents a topic model that captures the temporal dynamics in the text data along with topical phrases. Previous approaches have relied upon bag-of-words assumption to model such property in a corpus. This has resulted in an inferior performance with less interpretable topics. Our topic model can not only capture changes in the way a topic structure changes over time but also maintains important contextual information in the text data. Finding topical n-grams, when possible based on context, instead of always presenting unigrams in topics does away with many ambiguities that individual words may carry. We derive a collapsed Gibbs sampler for posterior inference. Our experimental results show an improvement over the current state-of-the-art topics over time model.