Online multi-label dependency topic models for text classification

Multi-label text classification is an increasingly important field as large amounts of text data are available and extracting relevant information is important in many application contexts. Probabilistic generative models are the basis of a number of popular text mining methods such as Naive Bayes o...

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Vydáno v:	Machine learning Ročník 107; číslo 5; s. 859 - 886
Hlavní autoři:	Burkhardt, Sophie, Kramer, Stefan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.05.2018 Springer Nature B.V
Témata:	Artificial Intelligence Bayesian analysis Classification Computer Science Control Data mining Dirichlet problem Labels Machine learning Mechatronics Natural Language Processing (NLP) Robotics Simulation and Modeling Text editing Texts Training LDA Multi-label classification Online learning Topic model
ISSN:	0885-6125, 1573-0565
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Abstract	Multi-label text classification is an increasingly important field as large amounts of text data are available and extracting relevant information is important in many application contexts. Probabilistic generative models are the basis of a number of popular text mining methods such as Naive Bayes or Latent Dirichlet Allocation. However, Bayesian models for multi-label text classification often are overly complicated to account for label dependencies and skewed label frequencies while at the same time preventing overfitting. To solve this problem we employ the same technique that contributed to the success of deep learning in recent years: greedy layer-wise training. Applying this technique in the supervised setting prevents overfitting and leads to better classification accuracy. The intuition behind this approach is to learn the labels first and subsequently add a more abstract layer to represent dependencies among the labels. This allows using a relatively simple hierarchical topic model which can easily be adapted to the online setting. We show that our method successfully models dependencies online for large-scale multi-label datasets with many labels and improves over the baseline method not modeling dependencies. The same strategy, layer-wise greedy training, also makes the batch variant competitive with existing more complex multi-label topic models.
AbstractList	Multi-label text classification is an increasingly important field as large amounts of text data are available and extracting relevant information is important in many application contexts. Probabilistic generative models are the basis of a number of popular text mining methods such as Naive Bayes or Latent Dirichlet Allocation. However, Bayesian models for multi-label text classification often are overly complicated to account for label dependencies and skewed label frequencies while at the same time preventing overfitting. To solve this problem we employ the same technique that contributed to the success of deep learning in recent years: greedy layer-wise training. Applying this technique in the supervised setting prevents overfitting and leads to better classification accuracy. The intuition behind this approach is to learn the labels first and subsequently add a more abstract layer to represent dependencies among the labels. This allows using a relatively simple hierarchical topic model which can easily be adapted to the online setting. We show that our method successfully models dependencies online for large-scale multi-label datasets with many labels and improves over the baseline method not modeling dependencies. The same strategy, layer-wise greedy training, also makes the batch variant competitive with existing more complex multi-label topic models.
Author	Burkhardt, Sophie Kramer, Stefan
Author_xml	– sequence: 1 givenname: Sophie orcidid: 0000-0002-5385-3926 surname: Burkhardt fullname: Burkhardt, Sophie email: burkhardt@informatik.uni-mainz.de organization: Institute of Computer Science, Johannes Gutenberg-University of Mainz – sequence: 2 givenname: Stefan surname: Kramer fullname: Kramer, Stefan organization: Institute of Computer Science, Johannes Gutenberg-University of Mainz
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Cites_doi	10.1111/j.1467-9868.2009.00698.x 10.21236/ADA440081 10.1007/978-3-540-74958-5_38 10.1162/neco.2006.18.7.1527 10.1007/978-3-642-12837-0_11 10.1145/2245276.2245311 10.1109/ICDM.2008.140 10.1016/j.patcog.2017.05.007 10.1198/016214506000000302 10.3115/1699510.1699543 10.1073/pnas.0307752101 10.1145/1143844.1143917 10.4018/jdwm.2007070101 10.1007/s10994-011-5256-5 10.1109/TKDE.2013.39 10.1007/978-3-662-44851-9_28 10.1007/s10994-011-5272-5
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References_xml	– reference: Li, A. Q., Ahmed, A., Ravi, S., & Smola, A. J. (2014). Reducing the sampling complexity of topic models. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14 (pp. 891–900). New York, NY, USA: ACM. – reference: Asuncion, A., Welling, M., Smyth, P., & Teh, Y. W. (2009). On smoothing and inference for topic models. In Proceedings of the 25th conference on uncertainty in artificial intelligence, UAI ’09 (pp. 27–34). Arlington, Virginia, United States: AUAI Press. – reference: HintonGEOsinderoSTehYWA fast learning algorithm for deep belief netsNeural Computation200618715271554222448510.1162/neco.2006.18.7.15271106.68094 – reference: Zhang, M. L., & Zhang, K. (2010). Multi-label learning by exploiting label dependency. In Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’10 (pp. 999–1008). 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Title	Online multi-label dependency topic models for text classification
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