Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
Table 3: Performance of EoG on the CDR test set with different pre-trained word embeddings.
['Embeddings', 'F1 (%) Overall', 'F1 (%) Intra', 'F1 (%) Inter']
We first analyse the performance of our main model (EoG) using different pre-trained word embeddings. Chiu et al. (GloVe) Pennington et al. As observed, our proposed model performs consistently with both in-domain and out-of-domain pre-trained word embeddings. The low performance of random embeddings is due to the small size of the dataset, which results in lower quality embeddings.