TY - GEN
T1 - Extraction of current actual status and demand expressions from complaint reports
AU - Sano, Yuta
AU - Mine, Tsunenori
N1 - Funding Information:
JSPS KAKENHI Grant Number 26350357, 26540183, 15H05708 and 16H02926.
Publisher Copyright:
© 2016 ACM.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - Government 2.0 activities have become very attractive and popular these days. Using platforms to support the activities such as FixMyStreet, SeeClickFix, or CitySourced, anyone can anytime report issues or complaints in a city with their photographs and geographical information on the Web, and share them with other people. On the other hand, unlike telephone calls, the concreteness of a report depends on its reporter; 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 understand the actual sta-tus or a demand to the status from the report. To solve the problems, it is indispensable to complement missing in-formation and estimate the actual status or the demand to the status from ambiguous information in the report. This paper proposes novel methods to detect segments related to an actual status and the demand to the status in a report. The methods combine empirical rules with several machine learning techniques that actively use dependency relation between words. Experimental results illustrate the validity of the proposed methods.
AB - Government 2.0 activities have become very attractive and popular these days. Using platforms to support the activities such as FixMyStreet, SeeClickFix, or CitySourced, anyone can anytime report issues or complaints in a city with their photographs and geographical information on the Web, and share them with other people. On the other hand, unlike telephone calls, the concreteness of a report depends on its reporter; 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 understand the actual sta-tus or a demand to the status from the report. To solve the problems, it is indispensable to complement missing in-formation and estimate the actual status or the demand to the status from ambiguous information in the report. This paper proposes novel methods to detect segments related to an actual status and the demand to the status in a report. The methods combine empirical rules with several machine learning techniques that actively use dependency relation between words. Experimental results illustrate the validity of the proposed methods.
UR - http://www.scopus.com/inward/record.url?scp=85014957961&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014957961&partnerID=8YFLogxK
U2 - 10.1145/3011141.3011201
DO - 10.1145/3011141.3011201
M3 - Conference contribution
AN - SCOPUS:85014957961
T3 - ACM International Conference Proceeding Series
SP - 149
EP - 153
BT - 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings
A2 - Indrawan-Santiago, Maria
A2 - Anderst-Kotsis, Gabriele
A2 - Steinbauer, Matthias
A2 - Khalil, Ismail
PB - Association for Computing Machinery
T2 - 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016
Y2 - 28 November 2016 through 30 November 2016
ER -