Semantic annotation is a task of annotating web pages with ontological information. The large volume of web content needs to be annotated before furthering the investigation of Semantic Web, and thus it is necessary to automate the process of annotation. Our empirical study shows that strong dependencies exist among different types of targeted instances. Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling the dependencies to do better annotation. However, as information on a Web page is not necessary linearly laid-out, the previous linear-chain CRFs have their limitations in semantic annotation. This paper is concerned with the issue of semantic annotation on hierarchically dependent data (Hierarchical Semantic Annotation). To better incorporate dependencies across the hierarchically laid-out information, this paper proposes a Tree-structured Conditional Random Fields (TCRFs). Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.