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.