Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
A critique and improvement of an evaluation metric for text segmentation
Computational Linguistics
The Journal of Machine Learning Research
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Model-based overlapping clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Pachinko allocation: DAG-structured mixture models of topic correlations
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
ACM SIGIR Forum
Latent Dirichlet Co-Clustering
ICDM '06 Proceedings of the Sixth International Conference on 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
Minimum cut model for spoken lecture segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Operations for learning with graphical models
Journal of Artificial Intelligence Research
A statistical model for topically segmented documents
DS'11 Proceedings of the 14th international conference on Discovery science
Legal document clustering with built-in topic segmentation
Proceedings of the 20th ACM international conference on Information and knowledge management
An unsupervised topic segmentation model incorporating word order
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
On handling textual errors in latent document modeling
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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This paper presents a statistical model for discovering topical clusters of words in unstructured text. The model uses a hierarchical Bayesian structure and it is also able to identify segments of text which are topically coherent. The model is able to assign each segment to a particular topic and thus categorizes the corresponding document to potentially multiple topics. We present some initial results indicating that the word topics discovered by the proposed model are more consistent compared to other models. Our early experiments show that our model clustering performance compares well with other clustering models on a real text corpus, which do not provide topic segmentation. Segmentation performance of our model is also comparable to a recently proposed segmentation model which does not provide document clustering.