Article

Text matching as image recognition

Authors:

Xueqi ChengAuthors Info & Claims

AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence

Pages 2793 - 2799

Published: 12 February 2016 Publication History

Abstract

Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.

References

[1]

Brown, P. F.; Pietra, V. J. D.; Pietra, S. A. D.; and Mercer, R. L. 1993. The mathematics of statistical machine translation: Parameter estimation. Computational linguistics 19(2):263-311.

Digital Library

[2]

Dahl, G. E.; Sainath, T. N.; and Hinton, G. E. 2013. Improving deep neural networks for lvcsr using rectified linear units and dropout. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 8609-8613. IEEE.

[3]

Dolan, W. B., and Brockett, C. 2005. Automatically constructing a corpus of sentential paraphrases. In Proc. of IWP.

[4]

Duchi, J.; Hazan, E.; and Singer, Y. 2011. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research 12:2121-2159.

Digital Library

[5]

Gao, J.; Pantel, P.; Gamon, M.; He, X.; Deng, L.; and Shen, Y. 2014. Modeling interestingness with deep neural networks. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.

[6]

Giles, R. C. S. L. L. 2001. Overfitting in neural nets: Back-propagation, conjugate gradient, and early stopping. In Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference, volume 13, 402. MIT Press.

[7]

Girshick, R.; Donahue, J.; Darrell, T.; and Malik, J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 580-587. IEEE.

Digital Library

[8]

Hardoon, D. R., and Shawe-Taylor, J. 2003. Kcca for different level precision in content-based image retrieval. In Proceedings of Third International Workshop on Content-Based Multimedia Indexing, IRISA, Rennes, France.

[9]

Hinton, G. E.; Srivastava, N.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2012. Improving neural networks by preventing co-adaptation of feature detectors. CoRR abs/1207.0580.

[10]

Hu, B.; Lu, Z.; Li, H.; and Chen, Q. 2014. Convolutional neural network architectures for matching natural language sentences. In Advances in Neural Information Processing Systems, 2042-2050.

Digital Library

[11]

Huang, P.-S.; He, X.; Gao, J.; Deng, L.; Acero, A.; and Heck, L. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Conference on Information and Knowledge Management, 2333-2338. ACM.

Digital Library

[12]

Jia, Y.; Shelhamer, E.; Donahue, J.; Karayev, S.; Long, J.; Girshick, R.; Guadarrama, S.; and Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093.

[13]

Kalchbrenner, N.; Grefenstette, E.; and Blunsom, P. 2014. A convolutional neural network for modelling sentences. CoRR abs/1404.2188.

[14]

LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278-2324.

[15]

Li, H., and Xu, J. 2014. Semantic matching in search. Foundations and Trends in Information Retrieval 7(5):343-469.

Digital Library

[16]

Lu, Z., and Li, H. 2013. A deep architecture for matching short texts. In Advances in Neural Information Processing Systems, 1367-1375.

Digital Library

[17]

Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013. Efficient estimation of word representations in vector space. CoRR abs/1301.3781.

[18]

Salton, G.; Fox, E. A.; and Wu, H. 1983. Extended boolean information retrieval. Communications of the ACM 26(11):1022-1036.

Digital Library

[19]

Shen, Y.; He, X.; Gao, J.; Deng, L.; and Mesnil, G. 2014. A latent semantic model with convolutional-pooling structure for information retrieval. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 101-110. ACM.

Digital Library

[20]

Simard, P. Y.; Steinkraus, D.; and Platt, J. C. 2003. Best practices for convolutional neural networks applied to visual document analysis. In 2013 12th International Conference on Document Analysis and Recognition, volume 2, 958-958. IEEE Computer Society.

Digital Library

[21]

Socher, R.; Huang, E. H.; Pennin, J.; Manning, C. D.; and Ng, A. Y. 2011. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In Advances in Neural Information Processing Systems, 801-809.

Digital Library

[22]

Williams, D. R. G. H. R., and Hinton, G. 1986. Learning representations by back-propagating errors. Nature 323-533.

[23]

Wu, W.; Li, H.; and Xu, J. 2013. Learning query and document similarities from click-through bipartite graph with metadata. In Proceedings of the sixth ACM international conference on WSDM, 687-696. ACM.

Digital Library

[24]

Xue, X.; Jeon, J.; and Croft, W. B. 2008. Retrieval models for question and answer archives. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, 475-482. ACM.

Digital Library

[25]

Zeiler, M. D., and Fergus, R. 2014. Visualizing and understanding convolutional networks. In Computer Vision–ECCV 2014. Springer. 818-833.

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Text matching as image recognition
1. Computing methodologies
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AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence

February 2016

4406 pages

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Association for the Advancement of Artificial Intelligence

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AAAI Press

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Published: 12 February 2016

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