Entity Relationship Extraction Based on Bi-LSTM and Attention Mechanism

The extraction methods based on deep learning can automatically learn sentence features without complex feature engineering. But most current methods ignore the mining of text semantics. Therefore, based on the existing research, considering that Bi-LSTM can capture the advantages of bidirectional semantic dependence and the attention mechanism can assign different weights to the semantic features of different functions, this paper combines the two to perform entity relationship extraction. Beside, in the feature extraction layer, four types of features, part-of-speech, entity recognition type, relative position and the context of entities are introduced. In order to obtain the main connection between entities, the shortest dependency path is also introduced.