Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined RepresentationsM.A. ALi and W. Wang are the co-corresponding authors.
Table 3: Ablation study for FGET-RR using Glove + Context Encoder
['[BOLD] Adjacency Matrix', '[BOLD] Model', 'strict', 'Wiki mac-F1', 'mic-F1', 'strict', 'OntoNotes mac-F1', 'mic-F1', 'strict', 'BBN mac-F1', 'mic-F1']
Note that we only report the results for the Glove embeddings along with the context encoders. A similar trend is observed by replacing the Glove embeddings and contextual encoders with the deep contextualized ELMO embeddings. Comparing the results for different adjacency matrices, we observe that the identity matrix didn’t play a significant role in improving the model performance. For randomly generated adjacency matrices, a decline in performance shows that when the structure in data is lost the graph convolution layers are no longer useful in de-noising the representations.