Government 2.0 activities have become attractive and popular these days. Using tools of their activities, anyone can report issues or complaints in a city on the Web with their photographs and geographical information, and share their information with other people. On the other hand, unlike telephone calls, the concreteness of a report depends on its reporter. Thus, the actual status and demand to the status may not be described clearly or either one may be miss-described in the report. It may accordingly happen that officials in the city management section can not grasp the actual status or demand to the status of the report. To solve the problems, automatic finding incomplete reports and completing missing information are indispensable. In this paper, we propose methods to detect parts related to an actual status or demand to the status in a report using empirical patterns, dependency relations, and several machine learning techniques. Experimental results show that an average F-score and an average accuracy score our methods achieved were 0.798 and 0.893, respectively. In addition, in our methods, RF achieved better results than SVM for both F-score and accuracy scores.
|ジャーナル||Transactions of the Japanese Society for Artificial Intelligence|
|出版ステータス||出版済み - 1 1 2017|
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