Nearly-Unsupervised Hashcode Representationsfor Relation Extraction
Table 1: Evaluation results from cross-corpus evaluation for (train, test) pairs of datasets, AIMed (A) and BioInfer (B). For each model, we report F1 score, and if available, precision, recall scores are also shown in brackets. For the adversarial neural models by ganin2016domain, evaluation on the datasets was provided by rios2018generalizing.
['[BOLD] Models', '[BOLD] (A, B)', '[BOLD] (B, A)']
In reference to Tab. We observe that our model, KLSH-NU-RF, obtains F1 score, 3 pts higher w.r.t. the most recent baseline, KLSH-RF. In comparison to the semi-supervised neural neural models, CNN-RevGrad, Bi-LSTM-RevGrad, Adv-CNN, Adv-Bi-LSTM, SSL-VAE, which use the knowledge of a test set just like our model, we gain 8-11 pts in F1 score. On the other hand, when evaluating on BioInfer dataset as a test set and AIMed as a training set, our model is in tie w.r.t. the adversarial neural model, Adv-Bi-LSTM, though outperforming the other three adversarial models and SSL-VAE, by large margins in F1 score. In comparison to KLSH-RF, we retain same F1 score, while gaining in recall by 6 pts at the cost of losing 2 pts in precision.