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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| Published in: | Machine learning Vol. 107; no. 5; pp. 859 - 886 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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New York
Springer US
01.05.2018
Springer Nature B.V |
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| 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. |
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| 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 |
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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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| Keywords | LDA Multi-label classification Online learning Topic model |
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| References | LewisDDYangYRoseTGLiFRcv1: A new benchmark collection for text categorization researchJournal of Machine Learning Research20045361397 RubinTChambersASmythPSteyversMStatistical topic models for multi-label document classificationMachine Learning2012881–2157208294260710.1007/s10994-011-5272-51243.68248 BishopCMPattern recognition and machine learning (information science and statistics)2006New YorkSpringer1107.68072 ZhangLShahSKakadiarisIHierarchical multi-label classification using fully associative ensemble learningPattern Recognition2017708910310.1016/j.patcog.2017.05.007 Canini, K. R., Shi, L., & Griffiths, T. L. (2009). Online inference of topics with latent dirichlet allocation. In Proceedings of the international conference on artificial intelligence and statistics (pp. 65–72). Ghamrawi, N., & McCallum, A. (2005). Collective multi-label classification. In Proceedings of the 14th ACM international conference on information and knowledge management (pp. 195–200). New York: ACM. HintonGEOsinderoSTehYWA fast learning algorithm for deep belief netsNeural Computation200618715271554222448510.1162/neco.2006.18.7.15271106.68094 TehYWJordanMIBealMJBleiDMHierarchical Dirichlet processesJournal of the American Statistical Association200610147615661581227948010.1198/0162145060000003021171.62349 Tsoumakas, G., & Vlahavas, I. (2007). Random k-labelsets: An ensemble method for multilabel classification. In J. N. Kok, J. Koronacki, R. L. d. Mantaras, S. Matwin, D. Mladenič, & A. Skowron (Eds.), Proceedings of ECML (pp. 406–417). Warsaw, Poland: Springer. 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). Washington, DC, USA: ACM. 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. 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. HoffmanMDBleiDMWangCPaisleyJStochastic variational inferenceJournal of Machine Learning Research2013141303134730819261317.68163 Loza Mencía, E., & Fürnkranz, J. (2010). Efficient multilabel classification algorithms for large-scale problems in the legal domain. In E. Francesconi, S. Montemagni, W. Peters, & D. Tiscornia (Eds.), Semantic processing of legal texts—Where the language of law meets the law of language, lecture notes in artificial intelligence (1st ed., Vol. 6036, pp. 192–215). Berlin: Springer. Prabhu, Y., & Varma, M. (2014). Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14 (pp. 263–272). New York, USA: ACM. ReadJPfahringerBHolmesGFrankEClassifier chains for multi-label classificationMachine Learning2011853333359310823710.1007/s10994-011-5256-5 ZhangMLZhouZHA review on multi-label learning algorithmsIEEE Transactions on Knowledge and Data Engineering20142681819183710.1109/TKDE.2013.39 Nam, J., Kim, J., Loza Mencía, E., Gurevych, I., & Fürnkranz, J. (2014). Large-scale multi-label text classification—Revisiting neural networks. In T. Calders, F. Esposito, E. Hüllermeier, & R. Meo (Eds.), Proceedings of ECML-PKDD, Part II (pp. 437–452). Berlin, Heidelberg: Springer. Wallach, H. M., Mimno, D. M., & McCallum, A. (2009). Rethinking lda: Why priors matter. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, A. Culotta (Eds.), Advances in neural information processing systems 22 (pp. 1973–1981). Curran Associates Inc. Wicker, J., Pfahringer, B., & Kramer, S. (2012). Multi-label classification using boolean matrix decomposition. In Proceedings of the 27th annual ACM symposium on applied computing, SAC ’12 (pp. 179–186). New York, USA: ACM. Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The author-topic model for authors and documents. In Proceedings of the 20th conference on uncertainty in artificial intelligence, UAI ’04 (pp. 487–494). Arlington, Virginia, United States: AUAI Press. CappOMoulinesEOn-line expectation-maximization algorithm for latent data modelsJournal of the Royal Statistical Society Series B2009713593613274990910.1111/j.1467-9868.2009.00698.x1250.62015 Papanikolaou, Y., Foulds, J. R., Rubin, T. N., & Tsoumakas, G. (2015). Dense distributions from sparse samples: Improved Gibbs sampling parameter estimators for LDA. ArXiv e-prints. Foulds, J., Boyles, L., DuBois, C., Smyth, P., & Welling, M. (2013). Stochastic collapsed variational bayesian inference for latent dirichlet allocation. In Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’13 (pp. 446–454). New York, USA: ACM. TsoumakasGKatakisIMulti-label classification: An overviewInternational Journal of Data Warehousing and Mining2007200711310.4018/jdwm.2007070101 Hoffman, M., Bach, F. R., & Blei, D. M. (2010). Online learning for latent dirichlet allocation. In Advances in neural information processing systems (pp. 856–864). BengioYLamblinPPopoviciDLarochelleHGreedy layer-wise training of deep networksAdvances in Neural Information Processing Systems200719153 AlSumait, L., Barbar, D., & Domeniconi, C. (2008). On-line lda: Adaptive topic models for mining text streams with applications to topic detection and tracking. In 2008 eighth IEEE international conference on data mining (pp. 3–12). Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. In Proceedings of the National Academy of Sciences of the United States of America (Vol. 101, pp. 5228–5235). National Academy of Sciences. Gouk, H., Pfahringer, B., & Cree, M. J. (2016). Learning distance metrics for multi-label classification. In 8th Asian conference on machine learning (Vol. 63, pp. 318–333). Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 conference on empirical methods in natural language processing: Volume 1, EMNLP ’09 (pp. 248–256). Stroudsburg, USA: Association for Computational Linguistics. Huang, S. J., & Zhou, Z. H. (2012). Multi-label learning by exploiting label correlations locally. In Proceedings of the twenty-sixth AAAI conference on artificial intelligence, AAAI’12 (pp. 949–955). Toronto, Ontario, Canada: AAAI Press. Li, W., & McCallum, A. (2006). Pachinko allocation: Dag-structured mixture models of topic correlations. In Proceedings of the 23rd international conference on machine learning (pp. 577–584). New York: ACM. WickerJTyukinAKramerSA Nonlinear Label Compression and Transformation Method for Multi-label Classification Using Autoencoders2016ChamSpringer International Publishing328340 Yen, I.E.H., Huang, X., Ravikumar, P., Zhong, K., & Dhillon, I. (2016). Pd-sparse: A primal and dual sparse approach to extreme multiclass and multilabel classification. In Proceedings of the 33rd international conference on machine learning (pp. 3069–3077). New York: ACM. Teh, Y. W., Newman, D., & Welling, M. (2006). A collapsed variational bayesian inference algorithm for latent dirichlet allocation. In Advances in neural information processing systems (pp. 1353–1360). 5689_CR9 5689_CR8 CM Bishop (5689_CR4) 2006 5689_CR5 O Capp (5689_CR6) 2009; 71 5689_CR7 5689_CR1 5689_CR24 5689_CR22 5689_CR2 5689_CR20 5689_CR21 G Tsoumakas (5689_CR28) 2007; 2007 5689_CR29 5689_CR27 L Zhang (5689_CR34) 2017; 70 Y Bengio (5689_CR3) 2007; 19 YW Teh (5689_CR26) 2006; 101 5689_CR35 5689_CR14 5689_CR33 5689_CR12 5689_CR31 5689_CR10 GE Hinton (5689_CR11) 2006; 18 5689_CR30 J Wicker (5689_CR32) 2016 5689_CR19 J Read (5689_CR23) 2011; 85 DD Lewis (5689_CR15) 2004; 5 5689_CR17 ML Zhang (5689_CR36) 2014; 26 5689_CR18 5689_CR16 T Rubin (5689_CR25) 2012; 88 MD Hoffman (5689_CR13) 2013; 14 |
| 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). Washington, DC, USA: ACM. – reference: Loza Mencía, E., & Fürnkranz, J. (2010). Efficient multilabel classification algorithms for large-scale problems in the legal domain. In E. Francesconi, S. Montemagni, W. Peters, & D. Tiscornia (Eds.), Semantic processing of legal texts—Where the language of law meets the law of language, lecture notes in artificial intelligence (1st ed., Vol. 6036, pp. 192–215). Berlin: Springer. – reference: ZhangMLZhouZHA review on multi-label learning algorithmsIEEE Transactions on Knowledge and Data Engineering20142681819183710.1109/TKDE.2013.39 – reference: BishopCMPattern recognition and machine learning (information science and statistics)2006New YorkSpringer1107.68072 – reference: HoffmanMDBleiDMWangCPaisleyJStochastic variational inferenceJournal of Machine Learning Research2013141303134730819261317.68163 – reference: Wicker, J., Pfahringer, B., & Kramer, S. (2012). Multi-label classification using boolean matrix decomposition. In Proceedings of the 27th annual ACM symposium on applied computing, SAC ’12 (pp. 179–186). New York, USA: ACM. – reference: Papanikolaou, Y., Foulds, J. R., Rubin, T. N., & Tsoumakas, G. (2015). Dense distributions from sparse samples: Improved Gibbs sampling parameter estimators for LDA. ArXiv e-prints. – reference: TsoumakasGKatakisIMulti-label classification: An overviewInternational Journal of Data Warehousing and Mining2007200711310.4018/jdwm.2007070101 – reference: Canini, K. R., Shi, L., & Griffiths, T. L. (2009). Online inference of topics with latent dirichlet allocation. In Proceedings of the international conference on artificial intelligence and statistics (pp. 65–72). – reference: TehYWJordanMIBealMJBleiDMHierarchical Dirichlet processesJournal of the American Statistical Association200610147615661581227948010.1198/0162145060000003021171.62349 – reference: Huang, S. J., & Zhou, Z. H. (2012). Multi-label learning by exploiting label correlations locally. In Proceedings of the twenty-sixth AAAI conference on artificial intelligence, AAAI’12 (pp. 949–955). Toronto, Ontario, Canada: AAAI Press. – reference: Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 conference on empirical methods in natural language processing: Volume 1, EMNLP ’09 (pp. 248–256). Stroudsburg, USA: Association for Computational Linguistics. – reference: RubinTChambersASmythPSteyversMStatistical topic models for multi-label document classificationMachine Learning2012881–2157208294260710.1007/s10994-011-5272-51243.68248 – reference: ZhangLShahSKakadiarisIHierarchical multi-label classification using fully associative ensemble learningPattern Recognition2017708910310.1016/j.patcog.2017.05.007 – reference: Hoffman, M., Bach, F. R., & Blei, D. M. (2010). Online learning for latent dirichlet allocation. In Advances in neural information processing systems (pp. 856–864). – reference: Gouk, H., Pfahringer, B., & Cree, M. J. (2016). Learning distance metrics for multi-label classification. In 8th Asian conference on machine learning (Vol. 63, pp. 318–333). – reference: Foulds, J., Boyles, L., DuBois, C., Smyth, P., & Welling, M. (2013). Stochastic collapsed variational bayesian inference for latent dirichlet allocation. In Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’13 (pp. 446–454). New York, USA: ACM. – reference: Yen, I.E.H., Huang, X., Ravikumar, P., Zhong, K., & Dhillon, I. (2016). Pd-sparse: A primal and dual sparse approach to extreme multiclass and multilabel classification. In Proceedings of the 33rd international conference on machine learning (pp. 3069–3077). New York: ACM. – reference: Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. In Proceedings of the National Academy of Sciences of the United States of America (Vol. 101, pp. 5228–5235). National Academy of Sciences. – reference: CappOMoulinesEOn-line expectation-maximization algorithm for latent data modelsJournal of the Royal Statistical Society Series B2009713593613274990910.1111/j.1467-9868.2009.00698.x1250.62015 – reference: Ghamrawi, N., & McCallum, A. (2005). Collective multi-label classification. In Proceedings of the 14th ACM international conference on information and knowledge management (pp. 195–200). New York: ACM. – reference: Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The author-topic model for authors and documents. In Proceedings of the 20th conference on uncertainty in artificial intelligence, UAI ’04 (pp. 487–494). Arlington, Virginia, United States: AUAI Press. – reference: WickerJTyukinAKramerSA Nonlinear Label Compression and Transformation Method for Multi-label Classification Using Autoencoders2016ChamSpringer International Publishing328340 – reference: LewisDDYangYRoseTGLiFRcv1: A new benchmark collection for text categorization researchJournal of Machine Learning Research20045361397 – reference: Prabhu, Y., & Varma, M. (2014). Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14 (pp. 263–272). New York, USA: ACM. – reference: BengioYLamblinPPopoviciDLarochelleHGreedy layer-wise training of deep networksAdvances in Neural Information Processing Systems200719153 – reference: Teh, Y. W., Newman, D., & Welling, M. (2006). A collapsed variational bayesian inference algorithm for latent dirichlet allocation. In Advances in neural information processing systems (pp. 1353–1360). – reference: Li, W., & McCallum, A. (2006). Pachinko allocation: Dag-structured mixture models of topic correlations. In Proceedings of the 23rd international conference on machine learning (pp. 577–584). New York: ACM. – reference: Tsoumakas, G., & Vlahavas, I. (2007). Random k-labelsets: An ensemble method for multilabel classification. In J. N. Kok, J. Koronacki, R. L. d. Mantaras, S. Matwin, D. Mladenič, & A. Skowron (Eds.), Proceedings of ECML (pp. 406–417). Warsaw, Poland: Springer. – reference: Wallach, H. M., Mimno, D. M., & McCallum, A. (2009). Rethinking lda: Why priors matter. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, A. Culotta (Eds.), Advances in neural information processing systems 22 (pp. 1973–1981). Curran Associates Inc. – reference: Nam, J., Kim, J., Loza Mencía, E., Gurevych, I., & Fürnkranz, J. (2014). Large-scale multi-label text classification—Revisiting neural networks. In T. Calders, F. Esposito, E. Hüllermeier, & R. Meo (Eds.), Proceedings of ECML-PKDD, Part II (pp. 437–452). Berlin, Heidelberg: Springer. – reference: AlSumait, L., Barbar, D., & Domeniconi, C. (2008). On-line lda: Adaptive topic models for mining text streams with applications to topic detection and tracking. In 2008 eighth IEEE international conference on data mining (pp. 3–12). – reference: ReadJPfahringerBHolmesGFrankEClassifier chains for multi-label classificationMachine Learning2011853333359310823710.1007/s10994-011-5256-5 – ident: 5689_CR17 – volume: 71 start-page: 593 issue: 3 year: 2009 ident: 5689_CR6 publication-title: Journal of the Royal Statistical Society Series B doi: 10.1111/j.1467-9868.2009.00698.x – ident: 5689_CR8 doi: 10.21236/ADA440081 – ident: 5689_CR29 doi: 10.1007/978-3-540-74958-5_38 – ident: 5689_CR21 – volume: 18 start-page: 1527 issue: 7 year: 2006 ident: 5689_CR11 publication-title: Neural Computation doi: 10.1162/neco.2006.18.7.1527 – volume: 14 start-page: 1303 year: 2013 ident: 5689_CR13 publication-title: Journal of Machine Learning Research – volume-title: Pattern recognition and machine learning (information science and statistics) year: 2006 ident: 5689_CR4 – ident: 5689_CR18 doi: 10.1007/978-3-642-12837-0_11 – ident: 5689_CR31 doi: 10.1145/2245276.2245311 – ident: 5689_CR27 – volume: 5 start-page: 361 year: 2004 ident: 5689_CR15 publication-title: Journal of Machine Learning Research – ident: 5689_CR33 – start-page: 328 volume-title: A Nonlinear Label Compression and Transformation Method for Multi-label Classification Using Autoencoders year: 2016 ident: 5689_CR32 – ident: 5689_CR12 – ident: 5689_CR5 – ident: 5689_CR35 – ident: 5689_CR7 – ident: 5689_CR1 doi: 10.1109/ICDM.2008.140 – volume: 70 start-page: 89 year: 2017 ident: 5689_CR34 publication-title: Pattern Recognition doi: 10.1016/j.patcog.2017.05.007 – ident: 5689_CR9 – volume: 101 start-page: 1566 issue: 476 year: 2006 ident: 5689_CR26 publication-title: Journal of the American Statistical Association doi: 10.1198/016214506000000302 – ident: 5689_CR22 doi: 10.3115/1699510.1699543 – volume: 19 start-page: 153 year: 2007 ident: 5689_CR3 publication-title: Advances in Neural Information Processing Systems – ident: 5689_CR10 doi: 10.1073/pnas.0307752101 – ident: 5689_CR14 – ident: 5689_CR20 – ident: 5689_CR24 – ident: 5689_CR16 doi: 10.1145/1143844.1143917 – volume: 2007 start-page: 1 year: 2007 ident: 5689_CR28 publication-title: International Journal of Data Warehousing and Mining doi: 10.4018/jdwm.2007070101 – volume: 85 start-page: 333 issue: 3 year: 2011 ident: 5689_CR23 publication-title: Machine Learning doi: 10.1007/s10994-011-5256-5 – volume: 26 start-page: 1819 issue: 8 year: 2014 ident: 5689_CR36 publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2013.39 – ident: 5689_CR19 doi: 10.1007/978-3-662-44851-9_28 – ident: 5689_CR30 – volume: 88 start-page: 157 issue: 1–2 year: 2012 ident: 5689_CR25 publication-title: Machine Learning doi: 10.1007/s10994-011-5272-5 – ident: 5689_CR2 |
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