Week 4: Word Embeddings

Week 4: Word Embeddings#

Overview#

Welcome to Week 4 of our course on Natural Language Processing (NLP) and Large Language Models (LLMs). This week, we’ll dive deep into the fascinating world of word embeddings, a crucial concept in modern NLP that revolutionized how we represent words for machine learning models.

Word embeddings are dense vector representations of words that capture semantic relationships in a continuous vector space. Unlike traditional one-hot encoding, word embeddings allow us to represent words in a way that preserves their meaning and relationships to other words.

Learning Objectives#

By the end of this week, you will be able to:

  1. Understand the concept of word embeddings and their importance in NLP

  2. Explain the key differences between Word2Vec, GloVe, and FastText embedding models

  3. Implement and train word embedding models using Gensim

  4. Visualize word embeddings to gain insights into semantic relationships

  5. Apply pre-trained word embeddings to various NLP tasks

Key Topics#

1. Introduction to Word Embeddings#

  • Limitations of traditional word representations (e.g., one-hot encoding)

  • The idea behind distributional semantics

  • Properties and benefits of word embeddings

2. Word2Vec#

  • CBOW (Continuous Bag of Words) and Skip-gram architectures

  • Training process and optimization techniques

  • Handling of rare words and subword information

3. GloVe (Global Vectors for Word Representation)#

  • Comparison with Word2Vec

  • Co-occurrence matrix and how GloVe leverages global statistics

  • Applications and use cases

4. FastText#

  • Subword embeddings and their advantages

  • Handling out-of-vocabulary words

  • Performance on morphologically rich languages

5. Practical Applications of Word Embeddings#

  • Using pre-trained embeddings in downstream NLP tasks

  • Fine-tuning embeddings for specific domains

  • Evaluating the quality of word embeddings

Practical Component#

In this week’s practical session, you will:

  • Install and set up Gensim library

  • Train Word2Vec models on a text corpus

  • Visualize word embeddings using dimensionality reduction techniques (e.g., t-SNE)

  • Explore semantic relationships and word analogies using trained embeddings

  • Compare the performance of different embedding models on a simple NLP task

Assignment#

Your assignment this week will involve:

  1. Training word embedding models (Word2Vec, GloVe, and FastText) on a provided dataset

  2. Visualizing and analyzing the learned embeddings

  3. Implementing a simple word analogy task using the trained embeddings

  4. Writing a short report comparing the performance and characteristics of each embedding model