Pub Date : 2014-03-23DOI: 10.1109/ICACSIS.2014.7065867
M. R. Shihab, A. A. Lestari
Business environment has shifted into a customer-oriented one, changing the way customers take part in creating value towards organizational increase of competitive advantages. More than ever, customers are considered as knowledge, leading to insights, innovations, and new ideas within a company. Knowledge acquisition, such as customer knowledge, is proven to be very difficult. This leads us to explore further concerning customer knowledge acquisition. Customer knowledge itself consists of knowledge `from', `for', and `about' customers. This research is aimed to analyze the impacts of the three customer knowledge types towards knowledge management benefits, which in turn will affect a company's performance. Business processes, customers, employees, finance, products, and market are six-aspects used to measure knowledge management benefits. This research had collected questionnaires from 50 respondents consisting of major banks and insurance companies in Indonesia. Then the verified data was analyzed using PLS technique. The results show that knowledge `for' customers is most favorable towards knowledge management benefits, followed by knowledge `from' customers, while knowledge `about' customers is found to give minimum impact towards knowledge management benefits.
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Pub Date : 1900-01-01DOI: 10.1109/icacsis.2014.7065845
This paper introduces implementation of Shallow-Parsing methods in Question Answering System of Natural Language Processing for Indonesian Automatic Customer Service. The main idea of the approach using Shallow-Parsing is indexing the answer. In this paper we present some steps of simple Information Extraction (IE) using Shallow-Parsing such as Part-of-Speech Tagging, IOB Tagging, Text Chunking, Predictive Annotation, Relation Finding, and Answer Retrieval. The main purpose of the task is to identify key information, key phrase and question phrase that contained in each question or answer. With that, information system can index the given question and retrieve the relevant answer to customer. The experiments of Automatic Customer Service reported in this paper show competitive results, given 100 questions; system can respond 89 questions with relevant answer correctly. This experiment shows that the accuracy of the Automatic Customer Service system is 89% of 100 given questions.
{"title":"Shallow parsing natural language processing implementation for intelligent automatic customer service system","authors":"","doi":"10.1109/icacsis.2014.7065845","DOIUrl":"https://doi.org/10.1109/icacsis.2014.7065845","url":null,"abstract":"This paper introduces implementation of Shallow-Parsing methods in Question Answering System of Natural Language Processing for Indonesian Automatic Customer Service. The main idea of the approach using Shallow-Parsing is indexing the answer. In this paper we present some steps of simple Information Extraction (IE) using Shallow-Parsing such as Part-of-Speech Tagging, IOB Tagging, Text Chunking, Predictive Annotation, Relation Finding, and Answer Retrieval. The main purpose of the task is to identify key information, key phrase and question phrase that contained in each question or answer. With that, information system can index the given question and retrieve the relevant answer to customer. The experiments of Automatic Customer Service reported in this paper show competitive results, given 100 questions; system can respond 89 questions with relevant answer correctly. This experiment shows that the accuracy of the Automatic Customer Service system is 89% of 100 given questions.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116846850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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