Relation extraction from clinical texts using domain invariant convolutional neural network
Table 5: Comparative performance of SVM and CNN with filter length [4,6] each with 100 filters
['[BOLD] Name', '[BOLD] P', '[BOLD] R', '[BOLD] F']
We could not compare our results directly with the state of the art results obtained on the i2b2 dataset as we did not have the complete dataset. The following features are used for each entity pair instance: Any word between relation arguments Any PoS tags between relation arguments. We used genia tagger for PoS Any bigram between relation arguments Word preceding first and second argument Any three words succeeding the first and second arguments Sequence of chunk tags between relation arguments. We used genia tagger for chunk tag String of words between relation arguments First and second argument type (problem, treatment and test) Order of argument type appeared in sentence Distance between two arguments in terms of number of words Presence of only punctuation sign between arguments. This way we prepared attribute-value and numerical features for each instances. Here again results shown are average over the 5-folds.