article · 2018

Agricultural Human-Machine Dialogue System Development Based on Semantic and Location Similarity of Short Text Model

Qingfeng Wei, Chenxue Zhong, Jun Yu, Changshou Luo, Lei Chen

Cited by 2Abstract and details only

Abstract

To the problem that the reply accuracy is low and its can't answer common chat topics at the same time in agricultural man-machine dialogue, a similarity model is proposed by a comprehensive consideration of semantic words and position words, a system development process is design that agricultural problems call the interface of agricultural knowledge database, non professional issues call the interface of universal chat database. Through the test of the agricultural consulting service robot system development, the accuracy rate of the advisory reply reached 86.1%. The satisfaction rate of the system reached 80.5%.

Cite this paper

APA 7

Wei, Q., Zhong, C., Yu, J., Luo, C., & Chen, L. (2018). Agricultural Human-Machine Dialogue System Development Based on Semantic and Location Similarity of Short Text Model. https://doi.org/10.1109/imcec.2018.8469594

MLA 9

Wei, Qingfeng, et al. “Agricultural Human-Machine Dialogue System Development Based on Semantic and Location Similarity of Short Text Model.” vol. 11, 2018, pp. 2029–32, https://doi.org/10.1109/imcec.2018.8469594.

Chicago

Wei, Qingfeng, Chenxue Zhong, Jun Yu, Changshou Luo, and Lei Chen. 2018. “Agricultural Human-Machine Dialogue System Development Based on Semantic and Location Similarity of Short Text Model.” https://doi.org/10.1109/imcec.2018.8469594.

Harvard

Wei, Q. et al. (2018) ‘Agricultural Human-Machine Dialogue System Development Based on Semantic and Location Similarity of Short Text Model’. Available at: https://doi.org/10.1109/imcec.2018.8469594.

BibTeX

@article{wei2018agricultural,
  title = {{Agricultural Human-Machine Dialogue System Development Based on Semantic and Location Similarity of Short Text Model}},
  author = {Wei, Qingfeng and Zhong, Chenxue and Yu, Jun and Luo, Changshou and Chen, Lei},
  volume = {11},
  pages = {2029--2032},
  year = {2018},
  doi = {10.1109/imcec.2018.8469594}
}

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