How to decide between NER and QA Model?

1
votes

I am completing a task involving NLP and transformers. I would like to identify relevant features in a corpus of text. If i was to extract the relevant features from job description for instance the tools that would be used at the job (powerpoint, excel, java, etc..) and the level of proficiency required would this task be better suited for a Named Entity Recognition model or a Question Answering model.

If I was to approach it like a NER task I would attach a label to all the relevant tools in the training data and hope it would generalize well. I could approach the problem simialrly as a QA model and ask things like "what tools does this job require" and supply a description as context.

I plan to use the transformers library unless I am missing a better tool for this task. There are many features I am looking to extract so not all may be as simple as grabbing keywords from a list (programming languages, microsoft office etc...).

Would one of these approaches be a better fit or am I missing a better way to approach the proble.

Any help appreciated. Thank you!

1
pythonnlpextractnamed-entity-recognitiontransformer

1 Answers

3
votes

From what you say, it seems it an entity recognition task. However, the questions you should ask and answer yourself are:

  • How will your user interact with the model?

    • Structured information → Entity recognition.
    • Chatbot → QA.

  • Is there a predefined set of entities that you are going to extract from the text?

    • Yes → entity recognition.
    • No → QA.

  • How do the training data you have for finetuning look like?

    • Only a few of them → Entity recognition.
    • Plenty of data, question-answer pair → QA.