Summary

In this chapter, we delved into the rapidly evolving field of NLP. We began by exploring word embeddings and their diverse applications. Our journey led us to experiment with solving the mystery-word game using genetic algorithms, where word embedding vectors served as the genetic chromosome. Following this, we ventured into n-grams and their role in document classification through a newsgroup message classifier. In this context, we harnessed the power of genetic algorithms to identify a compact yet effective subset of n-gram features derived from the dataset.

Finally, we endeavored to minimize the feature subset, aiming to gain insights into the classifier’s operations and interpret the factors influencing its predictions. In the next chapter, we will delve deeper into the realm of explainable and interpretable AI while applying genetic algorithms.