Relation extraction from clinical texts using domain invariant convolutional neural network
Table 4: Contribution of each features (filter size : [4,6] each with 100 filters)
['[BOLD] Name', '[BOLD] P', '[BOLD] R', '[BOLD] F']
In order to investigate the contribution of each feature in final result we gradually include one feature in our model and compared the performance. First we use only random vector (RV) representation along with entity types (T) (first row in the table) as a baseline for our comparison. Adding position features (2nd row) lead to approximately 15% increase in recall, 7% in precision and 11.7% in F1 score. However including PoS and Chunk features although improved recall and F1 score by 4.3% and 1.3% but precision was decreased by 3.6%. In the second set of experiments, we first use pre-trained word vectors along with entity types (4th row) and later repeated the similar experiments as previously. Here again, inclusion of position features improved the recall by more than 14% and F1 score by around 11%. This clearly indicates word position relative to the entities of interest plays important role in deciding their influence in the context. Further including PoS and Chunk features also led to performance improvement.