  The last decade has witnessed the success of the traditional feature-based
method on exploiting the discrete structures such as words or lexical patterns
to extract relations from text. Recently, convolutional and recurrent neural
networks has provided very effective mechanisms to capture the hidden
structures within sentences via continuous representations, thereby
significantly advancing the performance of relation extraction. The advantage
of convolutional neural networks is their capacity to generalize the
consecutive k-grams in the sentences while recurrent neural networks are
effective to encode long ranges of sentence context. This paper proposes to
combine the traditional feature-based method, the convolutional and recurrent
neural networks to simultaneously benefit from their advantages. Our systematic
evaluation of different network architectures and combination methods
demonstrates the effectiveness of this approach and results in the
state-of-the-art performance on the ACE 2005 and SemEval dataset.
