A neuralized feature engineering method for entity relation extraction

Making full use of semantic and structure information in a sentence is critical to support entity relation extraction. Neural networks use stacked neural layers to perform designated feature transformations and can automatically extract high-order abstract feature representations from raw inputs. However, because a sentence usually contains several pairs of named entities, the networks are weak when encoding semantic and structure information of a relation instance. In this paper, we propose a neuralized feature engineering approach for entity relation extraction. This approach enhances the neural network by manually designed features, which have the advantage of using prior knowledge and experience developed in feature-based models. Neuralized feature engineering encodes manually designed features into distributed representations to increase the discriminability of a neural network. Experiments show that this approach considerably improves the performance compared to that of neural networks or feature-based models alone, exceeding state-of-the-art performance by more than 8% and 16.5% in terms of F1-score on the ACE corpus and the Chinese literature text corpus, respectively.