Week 3: Fundamentals of Language Models

Week 3: Fundamentals of Language Models#

Overview#

This week, we delve into the foundational concepts of language models, which are essential components in many Natural Language Processing (NLP) applications. We will focus on N-gram models and statistical language models, providing you with a solid understanding of how machines can predict and generate human-like text.

Learning Objectives#

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

  1. Understand the concept and importance of language models in NLP

  2. Explain the theory behind N-gram models and their applications

  3. Implement simple N-gram models using Python

  4. Comprehend the basics of statistical language models

  5. Evaluate the performance of language models using perplexity

Key Topics#

1. Introduction to Language Models#

  • Definition and purpose of language models

  • Applications of language models in NLP tasks

  • Historical context and evolution of language modeling techniques

2. N-gram Models#

  • Concept of N-grams (unigrams, bigrams, trigrams, etc.)

  • Probability calculations in N-gram models

  • Advantages and limitations of N-gram models

  • Smoothing techniques for handling unseen N-grams

3. Statistical Language Models#

  • Probabilistic approach to language modeling

  • Maximum Likelihood Estimation (MLE)

  • Conditional probability in language models

  • Challenges in statistical language modeling (data sparsity, context limitation)

4. Implementation of N-gram Models#

  • Building N-gram models from text corpora

  • Generating text using N-gram models

  • Handling out-of-vocabulary words

5. Evaluation of Language Models#

  • Introduction to perplexity as an evaluation metric

  • Calculating perplexity for N-gram models

  • Interpreting perplexity scores

Practical Component#

In this week’s practical session, you will:

  • Implement a simple N-gram model from scratch using Python

  • Use NLTK to create and experiment with N-gram models

  • Generate text using your implemented N-gram model

  • Evaluate the performance of your models using perplexity

Assignment#

You will be given a text corpus and tasked with building N-gram models of various orders (unigram, bigram, trigram). You’ll need to implement the models, generate text using each model, and compare their performance using perplexity. Additionally, you’ll write a brief report discussing the strengths and weaknesses of each model based on your observations.

Looking Ahead#

The fundamental concepts of language models that you learn this week will serve as a stepping stone to more advanced topics in NLP. In the coming weeks, we’ll explore more sophisticated language modeling techniques, including neural network-based approaches and state-of-the-art transformer models.

Additional Resources#