This paper describes an annotation guideline for a temporal relation tagged corpus. Our goal is to construct a machine learnable model that automatically analyzes temporal events and relations between events. Since analyzing all combinations of events is inefficient, we examine use of dependency structure analysis to efficiently recognize meaningful temporal relations. We survey a small tagged data set to investigate the coverage of our method. Although the coverage of our methods is about 49%, we find that the dependency structure appears useful for reducing manual efforts in constructing a tagged corpus with temporal relations.