Integrating Deep Learning with Logic Fusion for Information Extraction
Table 2: Results of End-to-End RE on TREC.
['Setting', 'Model', 'Evaluation', 'Entity', 'Relation']
Existing works for this domain consists of 2 different settings and evaluations. The second setting requires both segmentation and entity typing. Evaluations include relaxed version where an entity is regarded as correct if at least one of its consisting words have the correct type prediction. The strict version only treats a predicted entity as correct given a complete match. When boundaries are given, our model could be easily modified to treat each entity as a single unit for type predictions. To show the effect of joint inference with logic rules, we construct a pipeline model by first predicting entities followed by relation predictions given fixed entity parameters. Another model (Pipeline+feat) further appends rule-based features as 1-hot vectors for relation prediction. Obviously, the pipeline model achieves inferior performance and simple features are far less expressive than logic rules. For both settings, our model achieves the best results with a large margin. Furthermore, we also test our model without the POS embedding, shown as “Ours (w/o) POS”, which still demonstrates some performance gain.