Relation extraction from the perspective of the frequency domain: frequency-domain aware gated graph attention network

In relation extraction task, graph attention network, as the dominant model, often faces the challenge of attention bias caused by complex semantic environment. Existing approaches ignore decoupling and build fine-grained models to filter and directly interact the multiple levels of information overlapping in word vectors (word, phrase, clause, and sentence level), but using methods that focus only on context (such as additional knowledge or structure). Ignoring overlapping multilevel information leads to limited performance improvement of the model for attention bias, but also increases the processing cost. To overcome this core limitation, we propose a model to decouple and process multiple levels of semantic information from the spectrum domain: Frequency-domain aware Gated Graph Attention Network (FD-GGAN-RE). The network first uses spectral decomposition to decouple contextual word vectors into spectral domain vectors containing different levels of semantic information. Then, use Frequency Feature Selective Gate layer to realize adaptive semantic filtering, reducing the influence of irrelevant semantics on the subsequent graph attention calculation. Final the Frequency-domain graph attention layer realizes the direct interaction of multiple levels of semantic information in the spectrum domain, avoiding the attention bias caused by the context graph attention mechanism interacting with word vectors containing multiple levels of semantic overlap. SemEval and KBP37 scored 90.33 and 69.06 respectively for F1, which was 27% faster than GATs while F1 scored 0.15 and 0.84 higher, respectively. Frequency graph attention visualization further demonstrates the model’s capability to capture complex key semantics, while presenting a frequency-based approach that holds potential for application in other natural language processing tasks.