Improved Relation Extraction withFeature-Rich Compositional Embedding Models
Table 7: Comparison of models on ACE 2005 out-of-domain test sets for the low-resource setting, where the gold entity spans are known but entity types are unknown. PM’13 is the results reported in plank_embedding_2013. “Linear+Emb” is the implementation of our method (4) in [Nguyen et al.2015]. The “Tree-kernel+Emb” methods are the enrichments of tree-kernels with embeddings proposed by nguyen-plank-grishman:2015:ACL-IJCNLP.
['[BOLD] Model', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
When no entity types are available, the performance of our fcm only model greatly decreases to 48.15%, which is consistent with our observation in the ablation tests. The baseline model also relies heavily on the entity types. After we remove all the hand-engineering features that contain entity type information, the performance of our baseline model drop to 40.62%, even lower than the reduced fcm only model.