Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning
Table 2: PR curve areas for BGRU, BGRU-WLA+EWA and BGRU+EWA on various datasets.
['Test Settings Dataset', 'PR Curve Area Original Data', 'PR Curve Area STP Data']
Regardless of the dataset that we employ, BGRU-WLA(+STP)+EWA outperforms BGRU(+STP). To be more specific, the PR curve area has a relative improvement of over 2.3%, which demonstrates that entity-wise hidden states in the BGRU present more precise relational features than other word states. (2) BGRU(+STP)+EWA achieves further improvements and outperforms the baseline by over 4.6%, because it considers more information than entity or relational words alone. Thus, it indicates that entity words are essential for relation extraction, but they can not represent features of the whole sentence without other words.