My problem is
I want to build a one class SVM classifier to identify the nouns/aspects from test file. The training file has list of nouns. The test has list of words.
This is what I've done:
I'm using Weka GUI and I've trained a one class SVM(libSVM) to get a model.
Now the model classifies those words in test file that the classifier identified as nouns in the generated model. Others are classified as outliers. ( So it is just working like a look up. If it is identified as noun in trained model, then 'yes' else 'no')
So how to build a proper classifier?. ( I meant the format of input and what it information it should contain?)
- I don't give negative examples in training file since it is one class.
- My input format is arff
- Format of training file is a set of word,yes
Format of test file is a set of word,?
EDIT My test file will have noun phrases. So my classifier's job is to get the nouns words from candidates in test file.