Recognition of Mobile Robot Natural Language Navigation Instructions Based on Word Embedding and SVM

doi:10.3969/j.issn.1006-2475.2017.09.005

Abstract

Abstract: A method based on word embedding and support vector machine (SVM) for the commands recognition of mobile robots navigation is proposed as a solution for the mobile robots navigation by natural language instructions. A language model with navigation features is trained by Skip-gram model. Feature vector is generated by word embedding and the additive combinatorial operation characteristics of Skip-gram model. Feature vector is used as the feature input of SVM to complete the task of the navigation command distinction. The method overcomes the problem that the artificial definition feature vector of SVM model is complicated and incomplete. Experiments show that this method has a good classification result and improves the average F1-measure by 2%.
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Key words: word embedding, support vector machine (SVM), natural language processing, mobile robots navigation

CLC Number:

TP391.1

WANG Si-yuan1, WANG Heng-sheng1,2, LIU Tong1. Recognition of Mobile Robot Natural Language Navigation Instructions Based on Word Embedding and SVM[J]. Computer and Modernization, 2017, 0(9): 24-28,55.

References

［1］ Wang Hengsheng, Ren Jin, Li Xiyin. Natural spoken instructions understanding for rescue robot navigation based on cascaded conditional random fields［C］// The 9th International Conference on Human System Interactions(HSI). 2016:216-222.
［2］李瀚清,房宁,赵群飞,等. 利用深度去噪自编码器深度学习的指令意图理解方法［J］. 上海交通大学学报, 2016,50(7):1102-1107.
［3］袁树明. 基于自然语言理解的车辆行驶指令抽取［D］. 北京:北京邮电大学, 2013.
［4］ Yuan Shuming, Wang Xiaojie. Research on automatic semantic classification of human-interaction instructions［C］// IEEE International Conference on Cloud Computing and Intelligent Systems. 2012:1414-1419.
［5］高胜男,孔令富,吴培良. 面向室内智能机器人的中文服务指令自主处理方法［J］. 机器人, 2015(4):424-434.
［6］ Hinton G E. Learning distributed representations of concepts［C］// Proceedings of the 8th Annual Conference of the Cognitive Science Society. 1986:1-12.
［7］ Xu Wei, Rudnicky A I. Can artificial neural networks learn language models?［C］// Proceedings of International Conference on Speech and Language Processing. 2000:202-205.
［8］ Bengio Y, Schwenk H, Senécal J S, et al. Neural probabilistic language models［J］. Journal of Machine Learning Research, 2003,3(6):1137-1155.〖JY〗〖HT5”K〗(下转第55页)〖HT〗〖ZK)〗〖FL)〗
［9］ Mnih A, Hinton G. A scalable hierarchical distributed language model［C］// Proceedings of the 21st International Conference on Neural Information Processing Systems. 2008:1081-1088.
［10］ Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality［J］. Advances in Neural Information Processing Systems, 2013,26:3111-3119.
［11］ Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space［J］. Eprint Arxiv, 2013, arXiv:1301.3781.
［12］ Socher R, Pennington J, Huang E H, et al. Semi-supervised recursive autoencoders for predicting sentiment distributions［C］// Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011:151-161.
［13］ Johnson R, Zhang Tong. Effective use of word order for text categorization with convolutional neural networks［J］. Eprint Arxiv, 2014, arXiv:1412.1058.
［14］ Lai Siwei, Liu Kang, Xu Liheng, et al. How to generate a good word embedding［J］. Intelligent Systems IEEE, 2016,3(2):1.
［15］ Morin F, Bengio Y. Hierarchical probabilistic neural network language model［C］// Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 2005:246-252.
［16］ Sergienya I, Schütze H. Distributional models and deep learning embeddings: Combining the best of both worlds［J］. Eprint Arxiv, 2013, arXiv:1312.5559.
［17］ Bottou L. Stochastic gradient learning in neural networks［C］// Proceedings of Neuro-Nimes. 1991.
［18］ Cohen W W, Schapire R E, Singer Y. Learning to order things［J］. Journal of Artificial Intelligence Research, 2011,10(1):243-270.
［19］张宁. 基于语义的中文文本预处理研究［D］. 西安:西安电子科技大学, 2011.
［20］ Cortes C, Vapnik V. Support vector machine［J］. Machine Learning, 1995,20(3):273-297.

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