Improving Relation Extraction by Pre-trained Language Representations
Table 5: SemEval single-model test set performance. † marks results reported in the corresponding papers. We report the mean and standard deviation across 5 randomly initialized runs.
['System', 'P', 'R', 'F1']
The result indicates that pre-training via a language modeling objective allows the model to implicitly capture useful linguistic properties for relation extraction, outperforming methods that rely on explicit lexical features (SVM Rink and Harabagiu Similarly, our model outperforms approaches that rely on explicit syntactic features such as the shortest dependency path and learned distributed representations of part-of-speech and named entity tags (e.g., BCRNN Cai et al. , DRNN Xu et al.