http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Nguyen Ha-Phuong-Truc (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Thi Ngoc Thao Pham,Ha Phuong Truc Nguyen,Thi My Ni Pham,Quang Than Tran,Hoanh Su Le ASCONS 2022 IJASC Vol.4 No.1
Background/Objectives: Automation is a trend currently, and chatbots are an excellent way for organizations to automate customer service duties. The medical profession has been put under immense strain during COVID-19 outbreaks as the number of patients increases rapidly, causing medical institutions and hospitals to be overcrowded. Users will find it challenging to schedule an appointment at a clinic, provide health advice, or update information due to this. Methods/Statistical analysis: A chatbot that supports users in advising, making appointments, and screening COVID-19 patients at clinics can be a valuable resource for both the user and the clinic in this situation. This paper developed a framework for developing a chatbot for clinics using a specific Frequent Answer Question (FAQ) dataset relating to COVID-19 and frequent diseases in Vietnam. In our study, the RASA framework was used with data collected from interviewing receptionists and website of clinics. Findings: Our chatbot can act as a counsellor, assisting patients with scheduling appointments, answering inquiries about symptoms of common illnesses, in particular, COVID-19 patient screening. In addition, we integrated our chatbot with Facebook Messenger and Zalo, two major social networking sites in Vietnam. Improvements/Applications: For specific implementation with each clinic, dataset enhancement and new services information update are necessary for broader applications.
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Development of an AI Chatbot to Support Admissions and Career Guidance for Universities
Le Hoanh Su,Truong Dang-Huy,Tran Thi-Yen-Linh,Nguyen Thi-Duyen-Ngoc,Ly Bao-Tuyen,Nguyen Ha-Phuong-Truc ASCONS 2020 INTERNATIONAL JOURNAL OF EMERGING MULTIDISCIPLINAR Vol.4 No.2
The vocational guidance and advising education enrollment are one of the most important tasks in the enrollment process and promote the quality and reputation of the University. Admissions counseling offices at Universities and Colleges play a major role in vocational guidance and advising education enrollment. However, the support of these units is limited by office hours, speed and advisory efficiency, and besides, handling and answering questions process may also encounter obstacles such as: overload, misinformation, problem with the transmission, language barriers, expressions, limited time, support resources,… Thus we decided to do research to understand this situation. Then creating a dataset supports vocational guidance and advising education enrollment activities. We also design and integrate chatbot into the school system to support the admissions counseling process.
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Thi My Ni PHAM,Thi Ngoc Thao PHAM,Ha Phuong Truc NGUYEN,Bao Tuyen LY,Truc Linh NGUYEN,Hoanh Su LE 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.5
Banking and finance is a broad term that incorporates a variety of smaller, more specialized subjects such as corporate finance, tax finance, and insurance finance. A virtual assistant that assists users in searching for information about banking and finance terms might be an extremely beneficial tool for users. In this study, we explored the process of searching for information, seeking opportunities, and developing a virtual assistant in the first stages of starting learning and understanding Vietnamese to increase effectiveness and save time, which is also an innovative business practice in Use-case Vietnam. We built the FIBA2020 dataset and proposed a pipeline that used Natural Language Processing (NLP) inclusive of Natural Language Understanding (NLU) algorithms to build chatbot applications. The open-source framework RASA is used to implement the system in our study. We aim to improve our model performance by replacing parts of RASA’s default tokenizers with Vietnamese tokenizers and experimenting with various language models. The best accuracy we achieved is 86.48% and 70.04% in the ideal condition and worst condition, respectively. Finally, we put our findings into practice by creating an Android virtual assistant application using the model trained using Whitespace tokenizer and the pre-trained language m-BERT.
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