Abstract
Compared to traditional networking which using command-line interfaces, intent-based networking abstracts network complexity and improves automation by eliminating manual configurations. It allows a user or administrator to send a simple request—using natural language—to plan, design and implement/operate the physical network which can improve network availability and agility. For example, an IT administrator can request improved voice quality for its voice-over-IP application, and the network can respond. For intent-based networking, the translation and validation system take a higher-level business policy (what) as input from end users and converts it to the necessary network configuration (how) by natural language understanding technology. In this chapter, we focus on how artificial intelligence technology can be used in the natural language understanding in translation and validation system. We firstly propose an effective model for the similarity metrics of English sentences. In the model, we first make use of word embedding and convolutional neural network (CNN) to produce a sentence vector and then leverage the information of the sentence vector pair to calculate the score of sentence similarity. Then, we propose the SM-CHI feature selection method based on the common method used in Chinese text classification. Besides, the improved CHI formula and synonym merging are used to select feature words so that the accuracy of classification can be improved and the feature dimension can be reduced. Finally, we present a novel approach which considers both the semantic and statistical information to improve the accuracy of text classification. The proposed approach computes semantic information based on HowNet and statistical information based on a kernel function with class-based weighting.
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References
Mikolov Tomas, Sutskever Ilya, Chen Kai, Corrado Greg S, Dean Jeff. Distributed Representations of Words and Phrases and their Compositionality. In: Burges C. J. C., Bottou L., Welling M., Ghahramani Z., Weinberger K. Q., eds. Advances in Neural Information Processing Systems 26, Curran Associates, Inc. 2013 (pp. 3111–3119).
C. Jiang, H. Zhang, R. Yong, and H. H. Chen, “Energy-efficient non-cooperative cognitive radio networks: Micro, meso, and macro views,” IEEE Communications Magazine, vol. 52, no. 7, pp. 14–20, 2014.
Mikolov Tomas, Chen Kai, Corrado Greg, Dean Jeffrey. Efficient Estimation of Word Representations in Vector Space. Computation and Language. 2013.
S. Lei, L. Zhou, Q. Peng, and H. Yao, “Openflow based spatial information network architecture,” in International Conference on Wireless Communications & Signal Processing, 2015.
Collobert Ronan, Weston Jason. A unified architecture for natural language processing: deep neural networks with multitask learning. In: International conference on machine learning:160–167; 2008.
Hu Baotian, Lu Zhengdong, Li Hang, Chen Qingcai. Convolutional neural network architectures for matching natural language sentences. In: International Conference on Neural Information Processing Systems:2042–2050; 2014.
Yin Wenpeng, Schütze Hinrich. Convolutional Neural Network for Paraphrase Identification. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies:901–911; 2015.
C. Jiang, C. Yan, and K. J. R. Liu, “Graphical evolutionary game for information diffusion over social networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 4, pp. 524–536, 2017.
wikipedia. tf-idf. https://en.wikipedia.org/wiki/Tf%E2%80%93idf.
Tai Kai Sheng, Socher Richard, Manning Christopher D. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. Computer Science. 2015;5(1): 36.
F. Xu, L. Rong, C. Zhao, H. Yao, and J. Zhang, “Congestion-aware signaling aggregation scheme for cellular based underwater acoustic sensor network,” in IEEE International Conference on Communication Workshop, 2015.
Kalchbrenner Nal, Grefenstette Edward, Blunsom Phil. A Convolutional Neural Network for Modelling Sentences. Meeting of the association for computational linguistics. 2014;655–665.
Kim Yoon. Convolutional Neural Networks for Sentence Classification. Empirical methods in natural language processing. 2014;1746–1751.
He Hua, Gimpel Kevin, Lin Jimmy. Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks. In: Conference on Empirical Methods in Natural Language Processing:1576–1586; 2015.
——, “Multi-channel sensing and access game: Bayesian social learning with negative network externality,” IEEE Transactions on Wireless Communications, vol. 13, no. 4, pp. 2176–2188, 2014.
He Hua, Lin Jimmy. Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies:937–948; 2016.
H. Yao, X. Sun, Z. Zhou, L. Tang, and L. Shi, “Joint optimization of subchannel selection and spectrum sensing time for multiband cognitive radio networks,” in International Symposium on Communications & Information Technologies, 2010.
S. Lei, Z. Zheng, T. Liang, H. Yao, and Z. Jing, “Ultra-wideband channel estimation based on Bayesian compressive sensing,” in International Symposium on Communications & Information Technologies, 2010.
Socher Richard, Karpathy Andrej, Le Quoc V., Manning Chris D., Ng Andrew Y.. Grounded compositional semantics for finding and describing images with sentences. Nlp.stanford.edu. 2013.
Mueller Jonas, Thyagarajan Aditya. Siamese recurrent architectures for learning sentence similarity. In: Thirtieth AAAI Conference on Artificial Intelligence:2786–2792; 2016.
C. Jiang, Y. Chen, Y. H. Yang, C. Y. Wang, and K. J. R. Liu, “Dynamic Chinese restaurant game: Theory and application to cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 13, no. 4, pp. 1960–1973, 2014.
T. Liang, Z. Zhou, L. Shi, H. Yao, J. Zhang, and Y. Ye, “Laplace prior based distributed compressive sensing,” in International 1st Conference on Communications & Networking in China, 2010.
Erk Katrin. A structured vector space model for word meaning in context. In: Conference on Empirical Methods in Natural Language Processing:897–906; 2008.
Z. Zhen, T. Jiang, W. S. Zhang, and H. Yao, “Analysis speech of polypus patients based on channel parameters and fuzzy logic systems,” in Seventh International Conference on Fuzzy Systems & Knowledge Discovery, 2010.
Lapata Mirella, Mitchell Jeff. Vector-based Models of Semantic Composition. 2008.
Mitchell J, Lapata M. Composition in distributional models of semantics. Cognitive Science. 2010;34(8):1388.
Turney Peter D. Domain and Function: A Dual-Space Model of Semantic Relations and Compositions. Journal of Artificial Intelligence Research. 2013;44(4):533–585.
Y. H. Yang, Y. Chen, C. Jiang, C. Y. Wang, and K. J. R. Liu, “Wireless access network selection game with negative network externality,” IEEE Transactions on Wireless Communications, vol. 12, no. 10, pp. 5048–5060, 2013.
H. Yao, Z. Zheng, L. He, and L. Zhang, “Optimal power allocation in joint spectrum underlay and overlay cognitive radio networks,” in International Conference on Cognitive Radio Oriented Wireless Networks & Communications, 2009.
Erk Katrin. Vector Space Models of Word Meaning and Phrase Meaning: A Survey. Language & Linguistics Compass. 2012;6(10):635–653.
Clarke Daoud. A Context-theoretic Framework for Compositionality in Distributional Semantics. Computational Linguistics. 2011;38(1):41–71.
Das Dipanjan, Smith Noah A. Paraphrase identification as probabilistic quasi-synchronous recognition. In: Joint Conference of the Meeting of the ACL and the International Joint Conference on Natural Language Processing of the Afnlp: Volume:468–476; 2009.
C. Jiang, C. Yan, and K. J. R. Liu, “Distributed adaptive networks: A graphical evolutionary game-theoretic view,” IEEE Transactions on Signal Processing, vol. 61, no. 22, pp. 5675–5688, 2013.
H. Yao, Z. Zheng, L. Zhang, L. He, T. Liang, and K. S. Kwak, “An efficient power allocation scheme in joint spectrum overlay and underlay cognitive radio networks,” in International Conference on Communications & Information Technologies, 2009.
Dolan Bill, Quirk Chris, Brockett Chris. Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources. In: International Conference on Computational Linguistics:350; 2004.
Nakov Preslav, Zesch Torsten, eds. Proceedings of the 8th International Workshop on Semantic Evaluation. The Association for Computer Linguistics 2014.
Marelli Marco, Bentivogli Luisa, Baroni Marco, Bernardi Raffaella, Menini Stefano, Zamparelli Roberto. Semeval-2014 Task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. In: Semeval 2014: International Workshop on Semantic Evaluation:16; 2014.
Zhao Jiang, Zhu Tiantian, Lan Man. ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment. In: International Workshop on Semantic Evaluation:271–277; 2014.
B. Li, Z. Zheng, W. Zou, K. S. Kwak, F. Wu, and H. Yao, “A nonlinear transform and its application in the optimum receiving of ultra narrow-band,” in International Conference on Communications & Information Technologies, 2009.
Bjerva Johannes, Bos Johan, Goot Rob Van Der, Nissim Malvina. The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity. In: SemEval-2014 Workshop; 2014.
Jimenez Sergio, Dueñas George Enrique, Baquero Julia, Gelbukh Alexander. UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment. In: Semeval; 2014.
Lai Alice, Hockenmaier Julia. Illinois-LH: A Denotational and Distributional Approach to Semantics. In: International Workshop on Semantic Evaluation:329–334; 2014.
Bestgen Yves. CECL: a New Baseline and a Non-Compositional Approach for the Sick Benchmark. In: International Workshop on Semantic Evaluation:160–165; 2014.
H. Yao, L. Zhang, Z. Ran, and Z. Zheng, “An efficient game-based competitive spectrum offering scheme in cognitive radio networks with dynamic topology,” in IEEE International Conference on Communication Technology, 2008.
Proisl Thomas, Evert Stefan, Greiner Paul, Kabashi Besim. SemantiKLUE: Robust Semantic Similarity at Multiple Levels Using Maximum Weight Matching. In: International Workshop on Semantic Evaluation:532–540; 2014.
Rus Vasile, Mccarthy Philip M., Lintean Mihai C., Mcnamara Danielle S., Graesser Arthur C. Paraphrase Identification with Lexico-Syntactic Graph Subsumption. In: International Florida Artificial Intelligence Research Society Conference, May 15–17, 2008, Coconut Grove, Florida, USA:201–206; 2008.
Blacoe William, Lapata Mirella. A comparison of vector-based representations for semantic composition. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning:546–556; 2012.
Z. Ran, L. Zhang, Y. Fei, H. Yao, and Z. Zheng, Balanced clustering multi-hop routing algorithm for LEACH protocol in wireless sensor networks, 2008.
Fernando Samuel, Stevenson Mark. A Semantic Similarity Approach to Paraphrase Detection. Computational Linguistics UK Annual Research Colloquium. 2008.
Yao Haipeng, Liu Chong, Zhang Peiying, Wang Luyao. A feature selection method based on synonym merging in text classification system. Eurasip Journal on Wireless Communications & Networking. 2017;2017(1):166.
C. Jiang, C. Yan, G. Yang, and K. J. R. Liu, “Joint spectrum sensing and access evolutionary game in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 12, no. 5, pp. 2470–2483, 2013.
Hwee Tou Ng, Wei Boon Goh, and Kok Leong Low. Feature selection, perceptron learning, and a usability case study for text categorization. In International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 67–73. ACM, 1997.
Yiming Yang and Jan O Pedersen. A comparative study on feature selection in text categorization. In Fourteenth International Conference on Machine Learning, pages 412–420. Morgan Kaufmann Publishers Inc, 1997.
Gui Chuan Feng and Shubin Cai. An improved feature extraction algorithm based on chi and mi. 2015.
Yan Tang and Ting Xiao. An improved χ 2 (chi) statistics method for text feature selection. In International Conference on Computational Intelligence and Software Engineering, pages 1–4. IEEE, 2009.
Thorsten Joachims. Text categorization with Support Vector Machines: Learning with many relevant features. Springer Berlin Heidelberg, 1998.
H. P. Yao, L. Y. Zhang, Z. Zhou, and X. U. Fang-Min, “A handoff algorithm based on grey prediction model for bluetooth network,” Radio Engineering of China, 2008.
C. Jiang, Y. Chen, K. J. R. Liu, and Y. Ren, “Renewal-theoretical dynamic spectrum access in cognitive radio networks with unknown primary behavior,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 3, pp. 406–416, 2013.
Ted Dunning. Accurate methods for the statistics of surprise and coincidence. Linguistics-74 Computational Dirk Geeraerts Stefan Grondelaers and, 19(1):61–74, 1993.
J. Tian and W. Zhao. Words similarity algorithm based on tongyici cilin in semantic web adaptive learning system. Journal of Jilin University, 28(06):602–608, 2010.
H. Yao, Z. Zhang, and Y. Liu, “Research on the embedded sim technology in internet of things,” Information & Communications Technologies, 2012.
Sijun Qin, Jia Song, Pengzhou Zhang, and Yue Tan. Feature selection for text classification based on part of speech filter and synonym merge. In International Conference on Fuzzy Systems and Knowledge Discovery, pages 681–685, 2015.
H. Yao, Z. Yang, H. Jiang, and L. Ma, “A scheme of ad-hoc-based d2d communication in cellular networks.” Adhoc & Sensor Wireless Networks, vol. 32, 2016.
Susan Dumais, John Platt, David Heckerman, and Mehran Sahami. Inductive learning algorithms and representations for text categorization. In Proceedings of the seventh international conference on Information and knowledge management, pages 148–155. ACM, 1998.
Quoc V. Le and Tomas Mikolov. Distributed representations of sentences and documents. 4:II–1188, 2014.
Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global vectors for word representation. In Conference on Empirical Methods in Natural Language Processing, pages 1532–1543, 2014.
Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144–152. ACM, 1992.
Stephan R Sain. The nature of statistical learning theory. Technometrics, 38(4):409–409, 1996.
Thorsten Joachims. Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning, pages 137–142. Springer, 1998.
Shun-ichi Amari and Si Wu. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6):783–789, 1999.
Jamal Abdul Nasir, Asim Karim, George Tsatsaronis, and Iraklis Varlamis. A knowledge-based semantic kernel for text classification. In International Symposium on String Processing and Information Retrieval, pages 261–266. Springer, 2011.
Berna Altınel, Banu Diri, and Murat Can Ganiz. A novel semantic smoothing kernel for text classification with class-based weighting. Knowledge-Based Systems, 89:265–277, 2015.
George Siolas and Florence d’Alché Buc. Support vector machines based on a semantic kernel for text categorization. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, volume 5, pages 205–209. IEEE, 2000.
Dimitrios Mavroeidis, George Tsatsaronis, Michalis Vazirgiannis, Martin Theobald, and Gerhard Weikum. Word sense disambiguation for exploiting hierarchical thesauri in text classification. In European Conference on Principles of Data Mining and Knowledge Discovery, pages 181–192. Springer, 2005.
C Fellbaum and G Miller. WordNet:An Electronic Lexical Database. MIT Press, 1998.
Yan-Lan Zhu, Jin Min, Ya-qian Zhou, Xuan-jing Huang, and Li-De Wu. Semantic orientation computing based on hownet. Journal of Chinese Information Processing, 20(1):14–20, 2006.
Pei-Ying Zhang. A hownet-based semantic relatedness kernel for text classification. Indonesian Journal of Electrical Engineering and Computer Science, 11(4):1909–1915, 2013.
Jiaju Mei. Tongyi ci cilin. Shangai cishu chubanshe, 1985.
Nicholas E. Evangelopoulos. Latent semantic analysis. Wiley interdisciplinary reviews. Cognitive science, 4(6):683, 2013.
Berna Altlnel, Murat Can Ganiz, and Banu Diri. A novel higher-order semantic kernel for text classification. In Electronics, Computer and Computation (ICECCO), 2013 International Conference on, pages 216–219. IEEE, 2013.
Berna Altinel, Murat Can Ganiz, and Banu Diri. A semantic kernel for text classification based on iterative higher-order relations between words and documents. In International Conference on Artificial Intelligence and Soft Computing, pages 505–517. Springer, 2014.
Berna Altinel, Murat Can Ganiz, and Banu Diri. A simple semantic kernel approach for svm using higher-order paths. In Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on, pages 431–435. IEEE, 2014.
Berna Altınel, Murat Can Ganiz, and Banu Diri. A corpus-based semantic kernel for text classification by using meaning values of terms. Engineering Applications of Artificial Intelligence, 43:54–66, 2015.
H. Yao, Y. Liu, and C. Fang, “An abnormal network traffic detection algorithm based on big data analysis.” International Journal of Computers, Communications & Control, vol. 11, no. 4, 2016.
Karen Sparck Jones. A statistical interpretation of term specificity and its application in retrieval. Journal of documentation, 28(1):11–21, 1972.
Gerard Salton and Christopher Buckley. Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5):513–523, 1988.
Youngjoong Ko and Jungyun Seo. Automatic text categorization by unsupervised learning. In Proceedings of the 18th conference on Computational linguistics-Volume 1, pages 453–459. Association for Computational Linguistics, 2000.
Verayuth Lertnattee and Thanaruk Theeramunkong. Analysis of inverse class frequency in centroid-based text classification. In Communications and Information Technology, 2004. ISCIT 2004. IEEE International Symposium on, volume 2, pages 1171–1176. IEEE, 2004.
Göksel Biricik, Banu Diri, Ahmet Co, et al. A new method for attribute extraction with application on text classification. In Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on, pages 1–4. IEEE, 2009.
GÖKSEL BİRİCİK, Banu Diri, and AHMET COŞKUN SÖNMEZ. Abstract feature extraction for text classification. Turkish Journal of Electrical Engineering & Computer Sciences, 20(Sup. 1):1137–1159, 2012.
Simon Parsons. Introduction to machine learning by ethem alpaydin, MIT press, 0-262-01211-1, 400 pp., $50.00/£ 32.95, 2005.
Nello Cristianini, John Shawe-Taylor, and Huma Lodhi. Latent semantic kernels. Journal of Intelligent Information Systems, 18(2-3):127–152, 2002.
Edward Loper and Steven Bird. Nltk: the natural language toolkit. In Acl-02 Workshop on Effective TOOLS and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 63–70, 2002.
Fabian Pedregosa, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, and Jake Vanderplas. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12(10):2825–2830, 2011.
Mohamed El Kourdi, Amine Bensaid, and Tajje Eddine Rachidi. Automatic arabic document categorization based on the naïve bayes algorithm. In The Workshop on Computational Approaches To Arabic Script-Based Languages, pages 51–58, 2004.
Mostafa M Syiam, Zaki T Fayed, and Mena B Habib. An intelligent system for Arabic text categorization. International Journal of Intelligent Computing and Information Sciences, 6(1):1–19, 2006.
Xiang Zhang, Junbo Zhao, and Yann Lecun. Character-level convolutional networks for text classification. pages 649–657, 2015.
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Yao, H., Jiang, C., Qian, Y. (2019). Intention Based Networking Management. In: Developing Networks using Artificial Intelligence. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-15028-0_6
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