Machine Learning Approaches for Natural Language Problems
Rachel Wagner-Kaiser, Tim Cerino · 2025
This chapter covers typical machine learning (ML) techniques and methods for common natural language processing (NLP) tasks, including key considerations in how to break down and approach an NLP problem. The aspects of an NLP task are decomposed into data quality, distribution, variability, labeling, feature selection, explainability, model selection, model complexity, parameter tuning, and compute, among others. A case study is used as an example to drive considerations of each of these aspects, as a technical approach is determined for the NLP solution.