Introduction to Natural Language Processing

4. Word Representations

After ensuring our text data is clean and standardized through preprocessing, the next step is to convert this data into a format that machine learning models can utilize. This process, known as text representation, involves turning raw text into numerical features. We’ll begin with the Bag-of-Words (BoW) model, a foundational technique for simplifying text into vectors for analysis and modeling.

Understanding how to represent words in a machine-readable format is crucial for various NLP tasks. This lesson introduces the Bag-of-Words model as a starting point for word representation.

Bag-of-Words Model

The Bag-of-Words model transforms text into numerical features, making it easier for machine learning algorithms to process. Here’s a step-by-step explanation of how it works:

  1. Vocabulary Creation:

    • The model constructs a vocabulary from all unique words in the dataset. This list of words serves as a reference for text representation.

  2. Word Counting:

    • For each document (e.g., a sentence or paragraph), the model counts the occurrences of each word from the vocabulary.

  3. Vector Representation:

    • Each document is represented as a vector where each element corresponds to a word in the vocabulary, and the value at each position is the count of that word in the document.

In essence, the Bag-of-Words model turns each document into a "bag" of words, focusing on word frequency rather than their order. This allows for easy comparison of documents based on word counts, even though it disregards word order and context.

Example of Bag-of-Words

Let’s see how the Bag-of-Words model works with a single sentence:

Sentence: "Machine learning is fascinating."

Steps:

  1. Create the Vocabulary:

    • From the sentence, the vocabulary is: ["Machine", "learning", "is", "fascinating"].

  2. Count Word Occurrences:

    • Count how many times each word appears in the sentence.

  3. Vector Representation:

    • Represent the sentence as a vector based on word counts.

Vector Representation: - Sentence Vector: [1, 1, 1, 1] - "Machine": 1 - "learning": 1 - "is": 1 - "fascinating": 1

Try it for yourself with this trinket:

This vectorization process enables effective analysis and modeling of text data.

Advantages

  • Simple and Easy to Implement: The BoW model is straightforward and easy to understand.
  • Effective for Basic Models: It works well for many basic text classification tasks.

Limitations

  • Ignores Word Order: The BoW model does not capture the sequence or context of words in a document.
BOW Disadvantage Image
  • Sparsity: The resulting vectors are often sparse, containing many zero values, which can be inefficient for large datasets.

Applications

  • Search Engines: Advanced word representations improve search results and relevance.
  • Recommendation Systems: Word embeddings help recommend products or content based on user preferences.
  • Chatbots and Virtual Assistants: Semantic understanding enhances the accuracy and relevance of responses.

Understanding and applying word representations is essential for developing effective NLP models and applications. By choosing the right representation technique, you can better capture the nuances of human language and enhance the performance of your models.