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https://hdl.handle.net/2440/115616
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dc.contributor.author | Jiang, H. | - |
dc.contributor.author | Zhou, R. | - |
dc.contributor.author | Zhang, L. | - |
dc.contributor.author | Wang, H. | - |
dc.contributor.author | Zhang, Y. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Proceedings of the ACM Conference on Information and Knowledge Management (CIKM 2017), 2017, vol.Part F131841, pp.1489-1498 | - |
dc.identifier.isbn | 9781450349185 | - |
dc.identifier.uri | http://hdl.handle.net/2440/115616 | - |
dc.description | Session 8B: Text Analysis | - |
dc.description.abstract | Determining appropriate statistical distributions for modeling text corpora is important for accurate estimation of numerical charac- teristics. Based on the validity of the test on a claim that the data conforms to Poisson distribution we propose Poisson decomposi- tion model (PDM), a statistical model for modeling count data of text corpora, which can straightly capture each document’s mul- tidimensional numerical characteristics on topics. In PDM, each topic is represented as a parameter vector with multidimensional Poisson distribution, which can be easily normalized to multino- mial term probabilities and each document is represented as mea- surements on topics and thereby reduced to a measurement vec- tor on topics. We use gradient descent methods and sampling al- gorithm for parameter estimation. We carry out extensive experi- ments on the topics produced by our models. The results demon- strate our approach can extract more coherent topics and is com- petitive in document clustering by using the PDM-based features, compared to PLSI and LDA. | - |
dc.description.statementofresponsibility | Haixin Jiang, Rui Zhou, Limeng Zhang, Hua Wang, Yanchun Zhang | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.rights | © 2017 Association for Computing Machinery. | - |
dc.source.uri | http://dx.doi.org/10.1145/3132847.3132942 | - |
dc.subject | Topic model; Poisson decomposition; statistical testing; text classi- fication; topic coherence | - |
dc.title | A topic model based on poisson decomposition | - |
dc.type | Conference paper | - |
dc.contributor.conference | ACM Conference on Information and Knowledge Management (CIKM 2017) (6 Nov 2017 - 10 Nov 2017 : Singapore, SINGAPORE) | - |
dc.identifier.doi | 10.1145/3132847.3132942 | - |
dc.publisher.place | New York, NY, USA | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP170104747 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Zhou, R. [0000-0001-6807-4362] | - |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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