首页> 外文OA文献 >Implementasi Vector Space Model dalam Pembangkitan Frequently Asked Questions Otomatis dan Solusi yang Relevan untuk Keluhan Pelanggan di UPT PUSKOM UNS
【2h】

Implementasi Vector Space Model dalam Pembangkitan Frequently Asked Questions Otomatis dan Solusi yang Relevan untuk Keluhan Pelanggan di UPT PUSKOM UNS

机译:在UPT PUSKOM UNS为客户投诉生成自动常见问题和相关解决方案的向量空间模型实现

摘要

UPT PUSKOM UNS as an service unit needs customer’s complaint handling. Customer’s complaints will be given solutions based on the past complaints which has similarity with the new complaints. Therefore, a method to calculate similarity between new complaint dan the past complaints is needed. The result of the calculation can be used for generating automatic Frequently Asked Questions (FAQ) and relevant solutions. There are some methods can be used for calculating document similarity, such as VSM. VSM is a method that has efficient procedure, easily represented dan can be implemented in document-matching. Therefore, in this research VSM in generating automatic FAQ and relevant solutions for customer’s complaint in UPT PUSKOM UNS will be used. Weighting term usedTerm Frequency-Inverse Document Frequency (TF-IDF) technique. Compared combinations are TF-IDF it self, logarithmic modified TF and logarithmic modified IDF. Similarity measure used cosine similarity. The results of this research are VSM algorithm with TF-IDF weighting can be used to generate automatic FAQ and the relevant solutions. Based on the accuracy calculation of each experiment can be concluded on a threshold 0.5, the combination of TF-IDF notation which has an average rating of highest accuracy and precision is TF-IDF, that is respectively 62.09% and 55.15%. Whereas in the threshold 0.65 that has average rating of the highest accuracy and precision is the first modification, which is respectively 83.18% and 68.35%. Besides that, the experiment using 171 data TF-IDF and threshold 0.65 can generate 27 FAQ, that is percentage70.37% is relevant.
机译:作为服务部门的UPT PUSKOM UNS需要处理客户的投诉。将根据过去的投诉为客户的投诉提供解决方案,这些投诉与新的投诉相似。因此,需要一种计算新投诉与过去投诉之间相似度的方法。计算结果可用于生成自动常见问题解答(FAQ)和相关解决方案。有一些方法可用于计算文档相似度,例如VSM。 VSM是一种具有高效过程的方法,可以在文档匹配中轻松实现并实现。因此,在这项研究中,将使用VSM生成自动FAQ,并在UPT PUSKOM UNS中使用针对客户投诉的相关解决方案。所使用的加权术语术语频率-文档反转频率(TF-IDF)技术。比较的组合是自身的TF-IDF,对数修饰的TF和对数修饰的IDF。相似度度量使用余弦相似度。这项研究的结果是,具有TF-IDF权重的VSM算法可用于生成自动FAQ和相关解决方案。根据每个实验的准确度计算,可以在阈值0.5上得出结论,TF-IDF标记的组合的TF-IDF分别为62.09%和55.15%。在阈值0.65中,具有最高准确度和精确度的平均评级是第一次修改,分别为83.18%和68.35%。除此之外,使用171个数据TF-IDF和阈值0.65进行的实验可以生成27个FAQ,这与百分比70.37%相关。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号