Joint learning of ontology and semantic parser from text
Table 6: Performance of various relation extraction models.
['[BOLD] Model', '[BOLD] Precision', '[BOLD] Converted Recall', '[BOLD] Recall', '[BOLD] Converted F1', '[BOLD] F1']
For training all or a maximum of 10.000 eligible relations was taken for each of 74 relation types. A 10-fold cross validation was performed for evaluation. The converted recall and converted F1 score presents recall and F1 on converted examples, which are the one, where relations were successfully converted into variable trees. The performance increases with each model, however the interpretability decreases. We also compared our method to the conditional random fields(CRF). In the CRF method, tokens from all layers with window size 7 were taken as features for sequence prediction. On the converted examples CRF achieved F1 score of 80.8, which is comparable to our best model’s (LRCL) F1 score of 80.0.