Combining Neural Networks and Log-linear Models to Improve Relation Extraction
This work addresses relation extraction for natural language processing applications, but it is incremental as it combines existing methods rather than introducing a fundamentally new approach.
The paper tackled the problem of relation extraction from text by combining traditional feature-based methods with convolutional and recurrent neural networks to leverage their respective advantages, resulting in state-of-the-art performance on the ACE 2005 and SemEval datasets.
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.