Application of improved multi-model fusion technology in customer service answering system

Guangmin WANG; Yaofeng WANG

doi:10.11959/j.issn.1000-0801.2018308

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Application of improved multi-model fusion technology in customer service answering system

Column:artificial intelligence technology and application | 更新时间：2024-06-05

- Application of improved multi-model fusion technology in customer service answering system
- Telecommunications Science Vol. 34, Issue 12, Pages: 110-116(2018)
- 作者机构：
  
  1. 中国电信股份有限公司上海研究院，上海 200122
  2. 华东师范大学计算机科学与软件工程学院，上海 200062
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018308
  CLC： TP391
- Published Online：2018-12，
  
  Published：20 December 2018
- 稿件说明：
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Guangmin WANG, Yaofeng WANG. Application of improved multi-model fusion technology in customer service answering system[J]. Telecommunications science, 2018, 34(12): 110-116.
DOI：

Guangmin WANG, Yaofeng WANG. Application of improved multi-model fusion technology in customer service answering system[J]. Telecommunications science, 2018, 34(12): 110-116. DOI： 10.11959/j.issn.1000-0801.2018308.

摘要

随着人工智能技术的发展，越来越多的公司采用机器客服代替人工客服。但若采用传统关键词模型，则机器客服准确率难以提高；若采用深度学习模型进行训练，则又面临用户问题是短文本时，模型训练和预测效果不佳的问题。针对这些问题，通过深入研究和多次试验，提出一种融合关键词模型和基于字向量的深度学习模型的算法。最后实现了模型的训练和预测，在与传统算法的准确率对比方面展现了优势。

Abstract

With the development of artificial intelligence(AI)

more and more companies use machine customer service instead of manual customer service.However

if the traditional keyword model is adopted

the accuracy of the machine customer service is difficult to improve.If the deep learning model is used

the predict result is poor when the user problem is short text.Aiming at these problems

an algorithm combining keyword model and deep learning model based on word vector was proposed.The training and prediction of the model was realized

and the advantages were shown in the comparison with the accuracy of the traditional algorithm.

关键词

Keywords

references

YU Z T . Chinese question classification based on support vector machine [J ] . Journal of South China University of Technology , 2005 , 33 ( 9 ): 25 - 27 .

LI X , ROTH D . Learning question classifiers:the role of semantic information [J ] . Natural Language Engineering , 1998 , 12 ( 3 ): 229 - 249 .

YUAN X J , SHI J X , NING H . Feature selection using syntactic and semantic information in question classification [J ] . Computer Engineering ＆ Applications , 2008 , 44 ( 33 ): 144 - 147 .

SUN J G , CAI D F,DE-XIN L V , et al . HowNet based Chinese question automatic classification [J ] . Journal of Chinese Information Processing , 2007 , 21 ( 1 ): 90 - 95 .

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