Summary

This module explored how computers can understand human language through word embeddings. We covered key concepts and approaches that have transformed natural language processing.

We started with basic one-hot encoding and progressed to distributed representations, learning how context shapes word meaning through the distributional hypothesis. We studied TF-IDF and matrix methods to understand word importance across documents, and used techniques like SVD to find patterns in document-term matrices.

We then explored neural approaches including Word2Vec, GloVe, and FastText, which each brought important innovations to the field. We also learned about SemAxis for analyzing relationships between words by creating semantic axes and visualizing word meanings.

Ethics was a key focus, as we examined how word embeddings can capture and perpetuate societal biases. We studied methods to detect and analyze these biases to ensure responsible use of NLP systems.

The field of word embeddings has evolved by building on previous work while solving limitations. Starting with TF-IDF for word importance, progressing through neural methods like Word2Vec, and continuing with GloVe and FastText, each approach has made valuable contributions.

These techniques enable many NLP applications like document classification and semantic analysis.