Combining Neural Networks and Log-linear Models to Improve Relation Extraction

TL;DR

This paper introduces a hybrid approach combining traditional feature-based methods with convolutional and recurrent neural networks to enhance relation extraction, achieving state-of-the-art results on benchmark datasets.

Contribution

It presents a novel combination of neural networks and feature-based methods, systematically evaluating architectures to improve relation extraction performance.

Findings

Achieved state-of-the-art results on ACE 2005 dataset.

Demonstrated the effectiveness of combining neural networks with traditional features.

Systematic evaluation of different network architectures and combination strategies.

Abstract

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.