Many researchers jointly model multiple linguistic tasks (e.g., joint modeling of named entity recognition and named entity classification and joint modeling of syntactic parsing and semantic parsing) with an implicit assumption that these individual tasks can enhance each other via the joint modeling. Before conducting research on jointly modeling multiple tasks, however, such researchers hardly examine whether such assumption is true or not. In this paper, we empirically examine whether named entity classification improves the performance of named entity recognition as an empirical case of examining whether semantics improves the performance of a syntactic task. To this end, we firstly specify the way to determine whether a linguistic task is a syntactic task or a semantic task according to both syntactic theory and semantic theory. After that, we design and conduct extensive experiments on two well-known benchmark datasets using three representative yet diverse state-of-the-art models. Experimental results demonstrate that named entity recognition does not lie at the semantic level and is not a semantic task; instead, it is a syntactic task and that the joint modeling of named entity recognition and classification does not improve the performance of named entity recognition. Experimental results also demonstrate that traditional handcrafted feature models can achieve state-of-the-art performance in comparison with the auto-learned feature model on named entity recognition.