Integrating Deep Learning with Logic Fusion for Information Extraction
Table 4: Comparisons on different model settings.
['Settings', 'Models', 'Res14 OTE', 'Res16 OTE', 'Lap14 OTE', 'TREC Entity', 'TREC Relation', 'ACE05 Entity', 'ACE05 Relation']
The objective is to analyze the effect of interactions within entities themselves, as well as the correlations between entities and relations. We first remove the relation prediction component to examine the results for solely entity extraction, denoted as ‘Entity’. We construct 3 different settings to compare the results among standard transformer model, removing label embedding (TransF−xl), and integrating only segmentation rules (TransF+SR) with discrepancy loss. As shown, the label embedding slightly improves all the performances which proves its ability to implicitly exploit the segmentation dependencies. However, more improvements are observed by explicitly incorporating logic rules. When jointly predicting entities and relations, we could observe slight improvements upon entity extraction using transformer. This shows positive correlations between entities and their relations. For this joint task, we examine the effect of distributed features (TransF−α) as well as symbolic rules (TransF+SR+RR) on the final results. By removing attention weight features, TransF−α achieves inferior results on relation predictions, demonstrating the advantage of attentions for relation modeling. More significant improvement could be observed when incorporating the relationships between entities and relations as explicit rules.