  Relation extraction is a key task in Natural Language Processing (NLP), which
aims to extract relations between entity pairs from given texts. Recently,
relation extraction (RE) has achieved remarkable progress with the development
of deep neural networks. Most existing research focuses on constructing
explicit structured features using external knowledge such as knowledge graph
and dependency tree. In this paper, we propose a novel method to extract
multi-granularity features based solely on the original input sentences. We
show that effective structured features can be attained even without external
knowledge. Three kinds of features based on the input sentences are fully
exploited, which are in entity mention level, segment level, and sentence
level. All the three are jointly and hierarchically modeled. We evaluate our
method on three public benchmarks: SemEval 2010 Task 8, Tacred, and Tacred
Revisited. To verify the effectiveness, we apply our method to different
encoders such as LSTM and BERT. Experimental results show that our method
significantly outperforms existing state-of-the-art models that even use
external knowledge. Extensive analyses demonstrate that the performance of our
model is contributed by the capture of multi-granularity features and the model
of their hierarchical structure. Code and data are available at
\url{https://github.com/xnliang98/sms}.
