From Corpus to Multi-Label Classification
Classifying large volumes of text data is a foundational skill in Natural Language Processing (NLP). However, instead of having a large labeled dataset available, you’ll often face a raw data set and the open-ended challenge of extracting value from it. There are many critical steps and concepts required for success in such scenarios and, unfortunately, they are often glossed over in academic courses and bootcamps.
In this series, drawing from my experience in data science, business strategy, and consulting, I’ll guide you through such a scenario while providing recommendations and code snippets to help you go from an unexplored corpus to a fully operational multi-label classification model. Along the way, we’ll touch on Inter-Annotator Agreement, the Entity-Aspect framework, BERTopic, and more.
I hope you find it helpful!
- Unexplored Corpus
- Useful Labels
- Stakeholder Alignment
- Annotating Examples
- Training Set
- Model Evaluation
- Model Deployment
Extras
Note: For our scenario, we’ll assume your company recently acquired another, inheriting a significant amount of data in the process. A business case justifying our efforts to classify the data was already approved by executives. This will allow us to focus on core NLP tasks, avoiding the preliminaries. However, the approaches discussed here can be adapted to a variety of situations.
