Abstract
In the human computer dialog system in vehicle environment, some dialogue entities are usually left out by human after several dialog turns. This causes troubles to classify user’s intention in a period of chat history. A usual solution for this problem is adding context information to expand the current question. This method causes a trend to generate multiple entities in the expanded question and decreases the classification accuracy of users’ intention. In this paper, an RNN based entity recognition model is built to recognize entities in the current problem. If the topic related entities are recognized, the intention and property are classified respectively using LDA and word2vec models; otherwise entities in context information are added to complete the question before intention classification. Experiments show that the proposed method has about 9.4% improvement in precision and 2.3% improvement in recall compared with the traditional context expansion method.
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Acknowledgments
This research work is supported by the grant of Guangxi science and technology development project (No: AC16380124, 1598018-6), the grant of Guangxi Key Laboratory of Cryptography & Information Security of Guilin University of Electronic Technology (No: GCIS201601), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics of Guilin University of Electronic Technology (No: GIIP201602), the grant of Guangxi Cooperative Innovation Center of Cloud Computing and Big Data of Guilin University of Electronic Technology (No: YD16E11), the grant of Guangxi Key Laboratory of Trusted Software of Guilin University of Electronic Technology (No: KX201514), the grant of Guangxi Experiment Center of Information Science of Guilin University of Electronic Technology (No: 20140208), the grant of Key Laboratory of Cloud Computing & Complex System of Guilin University of Electronic Technology (No: 15210).
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Zhang, K., Zhu, Q., Zhang, N., Shi, Z., Zhan, Y. (2017). User Intention Classification in an Entities Missed In-vehicle Dialog System. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_56
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DOI: https://doi.org/10.1007/978-3-319-61833-3_56
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