How to Annotate for Your NLP Pipeline?

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?

Source: Data Annotation and Labeling Services for Machine Learning

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:

1. 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.
2. 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.
3. 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…