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Documents from the same domain usually discuss similar topics in a similar order. However, the number of topics and the exact topics discussed in each individual document can vary. In this paper we present a simple topic model that uses generalised Mallows models and incomplete topic orderings to incorporate this ordering regularity into the probabilistic generative process of the new model. We show how to reparame-terise the new model so that a point-wise sampling algorithm from the Bayesian word segmentation literature can be used for inference. This algorithm jointly samples not only the topic orders and the topic assignments but also topic segmentations of documents. Experimental results show that our model performs significantly better than the other ordering-based topic models on nearly all the corpora that we used, and competitively with other state-of-the-art topic segmentation models on corpora that have a strong ordering regularity.
|Title of host publication||Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015|
|Place of Publication||Palo Alto, CA|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 1 Jun 2015|
|Event||29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States|
Duration: 25 Jan 2015 → 30 Jan 2015
|Other||29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015|
|Period||25/01/15 → 30/01/15|
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- 2 Finished
Johnson, M., Steedman, M., Newton, J., MQRES, M. & PhD Contribution (ARC), P. C. (.
31/07/11 → 31/12/15
31/07/11 → 30/06/16