There are a lot of explanations elsewhere, here I’d like to share some example questions in an interview setting.
How to annotate your data for your NLP pipeline?
Here are some tips for readers’ reference:
Annotating data is an important step in creating a Natural Language Processing (NLP) pipeline. It involves labeling different parts of your data such as text, speech, or images with relevant tags or metadata to enable your machine learning algorithms to learn from it. Here are some steps to follow when annotating your data:
- Define your annotation scheme: You need to decide on the type of annotations you want to use, such as Named Entity Recognition (NER), Part of Speech (POS), sentiment analysis, or topic modeling. Each type of annotation requires a different set of labels to annotate your data.
- Create a set of guidelines: It’s essential to create a set of guidelines or annotation instructions that describe how to annotate the data consistently. These guidelines should cover specific scenarios and edge cases that may arise during the annotation process.
- Choose an annotation tool: There are many annotation tools available, both free and paid, that can help you annotate your data. Some popular tools include Prodigy, Brat, and Label Studio. These tools allow you to label your data interactively and efficiently.
- Annotate the data: Once you have your annotation scheme, guidelines, and annotation tool ready, you can start annotating your data. You can either do it yourself or hire annotators to do it for you. It’s important to ensure that the annotation is accurate and consistent throughout the dataset.
- Validate the annotations: After annotating the data, it’s crucial to validate the annotations to ensure that they are accurate and consistent. You can use tools such as Kappa score or inter-annotator agreement to measure the reliability of the annotations.
- Train your NLP pipeline: Once you have your annotated data, you can use it to train your NLP pipeline. The pipeline will learn from the annotated data and make predictions on new data based on what it has learned.