I'm trying to solve a 'negation-like' classification problem, where I need to classify whether a certain word within the context has negative or positive label.
For example, how to identify whether a keyword has been prescribed or only mentioned:
['Aspirin has been prescribed to a patients'] -> {[('key_word': 'aspirin', 'prescribed': TRUE)]}
['If symptoms continue, the patient should consider taking Omeprazol'] -> {[('key_word': 'omeprazol', 'prescribed': FALSE)]}
[‘The plan is for him to commence 25mg of Trazodone as soon as he gets better.’] -> {[('key_word': 'trazodone ', 'prescribed': FALSE)]}
['her current meds are: sertraline 200 mg and olanzapine 5 mg'] -> {[('key_word': 'sertraline', 'prescribed': TRUE), ('key_word': 'olanzapine', 'prescribed': TRUE)]}
['if she continues to be depressed, then she needs to be started on Risperidone'] -> {[('key_word': 'risperidone', 'prescribed': FALSE)]}
I have a training data set for this task, but it is not clear how to formulate the classification problem. It is similar to sentiment classification problem, but here instead of predicting a label of the entire sentence, I need to predict a label of a certain keyword based on the context.
Any ideas?
key_word
to specify where the algorithm should be looking at (it shouldattend
to my key_word and make decision about it). $\endgroup$ – Arnold Klein Feb 11 '19 at 16:41