Chatbot Analytics Based on Question Answering System and Deep Learning: Case Study for Movie Smart Automatic Answering

We investigate the problem of complex answers in question answering. Complex answers consist of several simple answers. We describe the online question answering system SHAPAQA, and using data from this system we show that the problem of complex answers is quite common. We define nine types of complex questions, and suggest two approaches, based on answer frequencies, that allow question answering systems to tackle the problem.

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Academic Reader: An Interactive Question Answering System on Academic Literatures

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019855 ◽

2019 ◽

Vol 33 ◽

pp. 9855-9856

Author(s):

Yining Hong ◽

Jialu Wang ◽

Yuting Jia ◽

Weinan Zhang ◽

Xinbing Wang

Keyword(s):

Reading Comprehension ◽

Active Learning ◽

Question Answering ◽

Question Answering System ◽

Academic Literature ◽

Literature Reading ◽

Interactive Question ◽

Machine Reading

We present Academic Reader, a system which can read academic literatures and answer the relevant questions for researchers. Academic Reader leverages machine reading comprehension technique, which has been successfully applied in many fields but has not been involved in academic literature reading. An interactive platform is established to demonstrate the functions of Academic Reader. Pieces of academic literature and relevant questions are input to our system, which then outputs answers. The system can also gather users’ revised answers and perform active learning to continuously improve its performance. A case study is provided presenting the performance of our system on all papers accepted in KDD 2018, which demonstrates how our system facilitates massive academic literature reading.

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Application of Deep Learning in Video Question Answering System

Design of Intelligent Applications Using Machine Learning and Deep Learning Techniques ◽

10.1201/9781003133681-23 ◽

2021 ◽

pp. 353-372

Author(s):

Mansi Pandya ◽

Arnav Parekhji ◽

Aniket Shahane ◽

Palak V. Chavan ◽

Ramchandra S. Mangrulkar

Keyword(s):

Deep Learning ◽

Question Answering ◽

Question Answering System ◽

Video Question Answering

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Deep learning based question answering system in Bengali

Journal of Information and Telecommunication ◽

10.1080/24751839.2020.1833136 ◽

2020 ◽

pp. 1-34

Author(s):

Tasmiah Tahsin Mayeesha ◽

Abdullah Md Sarwar ◽

Rashedur M. Rahman

Keyword(s):

Deep Learning ◽

Question Answering ◽

Question Answering System

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A Study of Deep Learning for Factoid Question Answering System

2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI) ◽

10.1109/iri49571.2020.00070 ◽

2020 ◽

Cited By ~ 1

Author(s):

Min-Yuh Day ◽

Yu-Ling Kuo

Keyword(s):

Deep Learning ◽

Question Answering ◽

Question Answering System

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Research and Implementation of Railway Technical Specification Question Answering System Based on Deep Learning

2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) ◽

10.1109/itoec49072.2020.9141721 ◽

2020 ◽

Author(s):

Zhaohua Hu

Keyword(s):

Deep Learning ◽

Question Answering ◽

Technical Specification ◽

Question Answering System

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Intelligent Question Answering System of Medical Knowledge Map Based on Deep Learning

Advances in Intelligent Systems and Computing - The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy ◽

10.1007/978-3-030-62743-0_11 ◽

2020 ◽

pp. 75-80

Author(s):

Yafei Xue

Keyword(s):

Deep Learning ◽

Question Answering ◽

Medical Knowledge ◽

Knowledge Map ◽

Question Answering System

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Developing a Vietnamese Tourism Question Answering System Using Knowledge Graph and Deep Learning

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3453651 ◽

2021 ◽

Vol 20 (5) ◽

pp. 1-18

Author(s):

Phuc Do ◽

Truong H. V. Phan ◽

Brij B. Gupta

Keyword(s):

Deep Learning ◽

Natural Language ◽

Question Answering ◽

Knowledge Graph ◽

Test Set ◽

Tourist Attractions ◽

Question Answering System ◽

Tourism Websites

In recent years, Question Answering (QA) systems have increasingly become very popular in many sectors. This study aims to use a knowledge graph and deep learning to develop a QA system for tourism in Vietnam. First, the QA system replies to a user's question about a place in Vietnam. Then, the QA describes it in detail such as when the place was discovered, why the place's name was called like that, and so on. Finally, the system recommends some related tourist attractions to users. Meanwhile, deep learning is used to solve a simple natural language answer, and a knowledge graph is used to infer a natural language answering list related to entities in the question. The study experiments on a manual dataset collected from Vietnamese tourism websites. As a result, the QA system combining the two above approaches provides more information than other systems have done before. Besides that, the system gets 0.83 F1, 0.87 precision on the test set.

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