Inter-sentence Relation Extraction with Document-levelGraph Convolutional Neural Network
Table 2: Performance on the CDR and CHR test sets in comparison with the state-of-the-art.
['[BOLD] Data', '[BOLD] Model', '[BOLD] P (%)', '[BOLD] R (%)', '[BOLD] F1 (%)']
We report the performance of state-of-the-art models without any additional enhancements, such as joint training with NER, model ensembling and heuristic rules, to avoid any effects from the enhancements in the comparison. We observe that the GCNN outperforms the baseline models (CNN-RE/RNN-RE) in both datasets. However, in the CDR dataset, the performance of GCNN is 1.6 percentage points lower than the best performing system of gu2017. In fact, gu2017 incorporates two separate neural and feature-based models for intra- and inter-sentence pairs, respectively, whereas we utilize a single model for both pairs. Additionally, GCNN performs comparably to the second state-of-the-art neural model Li2018, which requires a two-step process for mention aggregation unlike our unified approach.