DOI: 10.14704/nq.2018.16.4.1214

Brain-oriented Cconvolutional Neural Network Computer Style Recognition of Classical Chinese Poetry

Jing Xuan, Zhongshi He, Liangyan Li, Weidong He, Fei Guo, Hang Zhang, Qiong Wu

Abstract


This paper aims to develop a feasible way to recognize the style of classical Chinese poetry with computers. To this end, the authors explored the connectionism in neuroscience, and explained the cognitive word embedding with the convolutional neural network (CNN). On the one hand, the genetic algorithm was adopted to extract keywords from traditional hand-labelled and selected information; on the other hand, a novel computer learning method was proposed based on text-to-image (T2I) CNN for big data. The proposed method was contrasted with the traditional genetic algorithm of naive Bayes and information gain. The experimental results show that our method achieved better classification accuracy with less human intervention than the traditional genetic algorithm. Hence, the CNN-based method is feasible on big data, both in theory and practice. This cross-disciplinary practice sheds light on stylistics, literature engineering, poetry cognition and neural network projects.

Keywords


Convolutional Neural Network (CNN), Classical Chinese Poetry, Cognitive Engineering, Connectionism

Full Text:

PDF

References


Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 2013; 35(8):1798-828.

Bengio Y, Ducharme R, Vincent P, Jauvin C. A neural probabilistic language model. Journal of machine Learning Research 2003; 3(Feb):1137-55.

Bird S, Klein E, Loper E. Natural Language Processing with Python; (J. Steele, Ed.) (1st ed.). Sebastopol, CA: O’Reilly Media, 2009.

Cadieu CF, Hong H, Yamins DL, Pinto N, Ardila D, Solomon EA, Majaj NJ, DiCarlo JJ. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS Computational Biology 2014; 10(12):e1003963.

Cai H, Wang L, Duan S. Sentiment classification model based on word embedding and CNN. Application Research of Computers 2016; 33(10): 7-11.

Classical Chinese Poetry. (n.d.). In Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Classical_Chinese_poetry (Retrieved on 26 August, 2017)

Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. Journal of Machine Learning Research 2011;12:2493-537.

Connectionism 2015; Retrieved from https://plato.stanford.edu/entries/connectionism/

Corneli J, Jordanous A, Shepperd R, Llano MT, Misztal J, Colton S, Guckelsberger C. Computational Poetry Workshop: Making Sense of Work in Progress. Proceedings of the 6th International Conference on Computational Creativity 2015; 268–75.

Delmonte R. Computing Poetry Style. In C. Battaglino, C. Bosco, E. Cambria, R. Damiano, V. Patti, & P. Rosso (Eds.), Proceeding ESSEM - Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM). Torino: CEUR Workshop Proceedings, 2013: 148-55.

Delmonte R. Exploring Shakespeare’s Sonnets with SPARSAR. Linguistics and Literature Studies 2016; 4(1): 61–95.

Ge W, Yu Y. Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning. InProc. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 2017; 6.

Hinton GE, Paccanaro A. Learning Distributed Representations of Concepts using Linear Relational Embedding. IEEE Transactions on Knowledge and Data Engineering 2001; 13(2): 232–44.

Hinton GE. Deep belief networks. Scholarpedia 2009; 4(5): 5947.

Hu R, Zhu Y. Automatic Classification of Tang Poetry Themes. Journal of Peking University (Science and Technology) 2015; 51(2): 262–68.

Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. In Advances In Neural Information Processing Systems 2012; 60(2): 1097–105.

Lecun Y, Bengio Y, Hinton GE. Deep Learning. Nature 2015; 521(7553): 436–44.

Li L, He Z, Yi Y. Poetry stylistic analysis technique based on term connections. Journal of Chinese Information Processing 2005; 19(6): 99–104.

Li L, He Z. General Model with Parameters Analysis of Syntax Tagging. Computer Science 2007; 34(11):189–92.

Li L. A study on term connection oriented NLP technique and its applications. Chongqing University, 2004.

Li L. Information dependency syntax tagging model. Shanghai: Academia Press, 2009.

Lin H. Toward Automated generation of chinese classic poetry. New Mathematics and Natural Computation. 2013; 9(02):153-81.

Liu C, Luo K. Tracking Words in Chinese Poetry of Tang and Song Dynasties with the China Biographical Database. arXive: Computation and Language 2016.

Liu CL. Quantitative analyses of Chinese poetry of Tang and Song dynasties: Using changing colors and innovative terms as examples. In Proc. of the International Confence on Digital Humanities 2013; 260–62.

Manurung R, Ritchie G, Thompson H. Using genetic algorithms to create meaningful poetic text. Journal of Experimental & Theoretical Artificial Intelligence 2012; 24(1):43-64.

Neurolinguistics. (n.d.). In Wikipedia. https://en.wikipedia.org/wiki/Neurolinguistics (Retrieved from 16 August, 2017)

Parkhi OM, Vedaldi A, Zisserman A. Deep Face Recognition. Proceedings of the British Machine Vision Conference 2015, (Section 3), 41.1–41.12.

Qian P, Huang X. Statistical Modeling and Macro Analysis on Chinese Classical Poems. Journal of Jiangxi Normal University (Natural Science) 2015; 39(2): 117–23.

Rahgozar A, Inkpen D. Poetry Chronological Classification: Hafez. In Canadian Conference on Artificial Intelligence. Springer, Cham 2016; 9673: 131–36.

Selangor B. Poetry Classification Using Support Vector Machines. Journal of Computer Science 2012; 8(9): 1441–46.

Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Sifre L. Mastering the game of Go without human knowledge. Nature 2017; 550: 354–59.

Srivastava N, Salakhutdinov R. Multimodal Learning with Deep Boltzmann Machines. In Advances in neural information processing systems (NIPS) 2012;2222–30.

Sundararajan L. Twenty-Four Poetic Moods: Poetry and Personality in Chinese Aesthetics. Creativity Research Journal 2004; 16(2-3): 201–14.

Wang Q, Luo T, Wang D. Can machine generate traditional Chinese poetry? A feigenbaum test. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 2016; 10023 LNAI(100084): 34–46.

Wang Q. The Expressive Forms of Natural Imagery in Chinese Poetry. Advances in Literary Study 2017; 5(1): 17–21.

Weston J, Bengio S, Usunier N. Large scale image annotation: Learning to rank with joint word-image embeddings. Machine Learning 2010; 81(1): 21–35.

Yi Y, Zheng Y, He Z, Li L. Studies of Traditional Chinese Poet Identification Based on Machine Learning. Mind and Computation 2017; 1(60173060): 359–64.

Yi Y. A Study on Style Identification and Chinese Couplet Responses Oriented Computer Aided Poetry Composing; Chongqing University, 2005.

Yi X., Li R., Sun M. (2017) Generating Chinese Classical Poems with RNN Encoder-Decoder. In: Sun M., Wang X., Chang B., Xiong D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL 2017, NLP-NABD 2017. Lecture Notes in Computer Science, vol 10565. Springer, Cham

Zhang X, Lapata M. Chinese Poetry Generation with Recurrent Neural Networks. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14) 2014; 670–80.

Zhou C, You W, Ding X. Genetic algorithm and its implementation of automatic generation of Chinese songci. Journal of Software 2010; 21(3): 427–37.


Supporting Agencies





| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2017