We compared two methods to annotate a corpus via non-expert annotators for named entity (NE) recognition task, which are (1) revising the results of the existing NE recognizer and (2) annotating NEs only by hand. We investigated the annotation time, the degrees of agreement, and the performances based on the gold standard. As we have two annotators for one file of each method, we evaluated the two performances, which are the averaged performances over the two annotators and the performances deeming the annotations correct when either of them is correct. The experiments revealed that the semi-automatic annotation was faster and showed better agreements and higher performances on average. However they also indicated that sometimes fully manual annotation should be used for some texts whose genres are far from its training data. In addition, the experiments using the annotated corpora via semi-automatic and fully manual annotation as training data for machine learning indicated that the F-measures sometimes could be better for some texts when we used manual annotation than when we used semi-automatic annotation.