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An Approach for Building a Semi-automatic Online Consultancy System

机译:一种建立半自动在线咨询系统的方法

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This study proposes an approach for building a semi-automatic consultancy (question-answering) system via mobile/Internet networks. This approach is a combination of natural language (text) processing and machine learning method. For building the system, at first, we need to build modules for sending and receiving messages via SMS/Email/Webpage. These modules are used for users to send/receive their questions that need to be consulted. While waiting for answering from the system, the user will be recommended similar questions which have been answered in the past by using Cosine similarity. Next, a message classification module is built using a combination of text processing (e.g., word segmentation, stop word deletion) and machine learning method (e.g., SVM). Finally, a whole web-based system is conducted to integrate these modules. The proposed approach is applied for a case study of consulting on Vietnam National Entrance Test, which is an important test for the pupils to get into universities. Initial results show that the system can automatically classify the questions at 82.33% of accuracy, thus, this approach could be promising for (semi) automatic online consultancy systems.
机译:本研究提出了一种通过移动/互联网网络构建半自动咨询(问答)系统的方法。这种方法是自然语言(文本)处理和机器学习方法的组合。首先为系统构建系统,我们需要通过短信/电子邮件/网页构建用于发送和接收邮件的模块。这些模块用于用户发送/接收需要咨询的问题。在等待系统的回答时,用户将推荐使用余弦相似性在过去回答的类似问题。接下来,使用文本处理(例如,单词分段,停止字删除)和机器学习方法(例如,SVM)的组合构建消息分类模块。最后,进行整个基于Web的系统以集成这些模块。拟议的方法适用于越南国家入学考试咨询的案例研究,这是对学生进入大学的重要考验。初始结果表明,该系统可以自动将问题分类为82.33%的准确性,因此,这种方法可能对(半)自动在线咨询系统有望。

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