Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages
7. Conclusions and Future Work
6. Discussion
The question-answering system proposed in this study is based on the dataset that we collected from social media in the event of natural disasters. This research aims to develop a system to support foreigners residing in Japan to obtain necessary real-time information during disasters. We have developed a system that continuously develops the previous study in which we investigated the social media messages from Twitter. We extracted sentences based on keywords and classify social media messages. We think that the crucial component of the system is the size of the social media dataset and the preciseness of the sentence classification. Specifically, we used the ontologies to classify texts as keywords to related categories in the classification process.
Moreover, the number of categories is important because it is used to calculate the meaning of the sentence by ontology and word similarity. After all, messages in social networks, such as Twitter, have only 140–280 characters. It is difficult to find the meaning of a sentence when the number of words is insufficient. Therefore, increasing the accuracy of finding the sentence’s meaning depends on the number of categories and further improved ontology databases.
The system proposed in this paper is designed to only be used in the event of a disaster. For building a good system, it is important to consider ease of use and familiarity in the event of disasters. From that perspective, we think that it is desirable to improve our system to be a dual-purpose information system, which is capable of continuous use during normal times and natural disasters [29,30]. Since users utilize the system for collecting information daily, they can collect information without confusion when a disaster occurs.
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