地域苦情データにおける現状，要望表現の識別

• 佐野 優太

  九州大学大学院システム情報科学府情報知能工学専攻

• 峯 恒憲

  九州大学大学院システム情報科学研究院情報知能工学部門

書誌事項

タイトル別名

• Detection of Current Actual Status and Demand Expressions in Community Complaint Reports

抄録

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.

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 32 (5), AG16-B_1-10, 2017

  一般社団法人 人工知能学会

キーワード

• dependency relations
• empirical patterns
• Government 2.0
• machine learning

詳細情報

• CRID: 1390282680084856064
• NII論文ID: 130006039510
• DOI: 10.1527/tjsai.ag16-b
• ISSN: 13468030
• ISSN: 13460714
• Web Site: https://www.jstage.jst.go.jp/article/tjsai/32/5/32_AG16-B/_pdf
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可