Adaptive feature extraction for entity relation extraction

Effective capturing of semantic dependencies within sentences is pivotal to support relation extraction. However, challenges such as feature sparsity, and the complexity of identifying the structure of target entity pairs brought by the traditional methods of feature extraction pose significant obstacles for relation extraction. Existing methods that rely on combined features or recurrent networks also face limitations, such as over-reliance on prior knowledge or the gradient vanishing problem. To address these limitations, we propose an Adaptive Feature Extraction (AFE) method, combining neural networks with feature engineering to capture high-order abstract and long-distance semantic dependencies. Our approach extracts atomic features from sentences, maps them into distributed representations, and categorizes these representations into multiple mixed features through adaptive combination, setting it apart from other methods. The proposed AFE-based model uses four different convolutional layers to facilitate feature learning and weighting from the adaptive feature representations, thereby enhancing the discriminative power of deep networks for relation extraction. Experimental results on the English datasets ACE05 English, SciERC, and the Chinese datasets ACE05 Chinese, and CLTC(SanWen) demonstrated the superiority of our method, the F1 scores were improved by 4.16%, 3.99%, 0.82%, and 1.60%, respectively. In summary, our AFE method provides a flexible, and effective solution to some challenges in cross-domain and cross-language relation extraction.