Module 2: Vector Representations
Module Overview
In this module, we'll explore vector representations of text data, a crucial step in making text processable by machine learning algorithms. We'll learn how to convert documents into numerical vectors, measure similarity between documents, and apply word embedding models to capture semantic relationships between words. These techniques form the foundation for document retrieval, recommendation systems, and more advanced NLP applications.
Learning Objectives
- Represent a document as a vector
- Query documents by similarity
- Apply word embedding models
Guided Project
Open DS_412_Vector_Representations_Lecture_GP.ipynb in the GitHub repository to follow along with the guided project.
Module Assignment
Work with job listings data for Data Scientists to practice text vectorization techniques. Create document-term matrices, implement TF-IDF vectorization, and build a nearest neighbor model to find similar job listings based on queries.
Assignment Solution Video
Additional Resources
Vector Representations and Embeddings
- Scikit-Learn: Text Feature Extraction
- Gensim: Word2Vec Tutorial
- Stanford NLP: GloVe - Global Vectors for Word Representation
Document Similarity and Retrieval
- Vector Semantics and Embeddings - Chapter from Speech and Language Processing
- Calculating Document Similarities Using BERT and Other Models