It is not immediately clear how to modify this repository for NER on an unlabeled data set with new classes. For example, the files ada_dict_generation.py and adaptive_pu_model.py both require model files from from supervised training output and from labeled data.
However, the approach in the paper describes the benefit of the proposed novel solution as being able to infer NER instances without training data.
How can this code be modified to support inference of novel NER classes in the absence of labeled data? i.e. What steps must be taken to modify this code to enable training and inference without any labeled data? Or, do I misunderstand the paper (is there always a requirement for some supervised training before the approach can then be used on unlabeled data?)
It is not immediately clear how to modify this repository for NER on an unlabeled data set with new classes. For example, the files
ada_dict_generation.pyandadaptive_pu_model.pyboth require model files from from supervised training output and from labeled data.However, the approach in the paper describes the benefit of the proposed novel solution as being able to infer NER instances without training data.
How can this code be modified to support inference of novel NER classes in the absence of labeled data? i.e. What steps must be taken to modify this code to enable training and inference without any labeled data? Or, do I misunderstand the paper (is there always a requirement for some supervised training before the approach can then be used on unlabeled data?)