Introduction to Natural Language Processing

As we reach the final stages of our NLP course, let's dive into two important methods for representing words: Word2Vec and GloVe. These models are crucial for understanding how computers process and understand human language.

What is Word2Vec?

Word2Vec is a neural network-based model that learns to represent words as dense vectors in a continuous vector space. These vectors capture semantic relationships between words, meaning that words with similar meanings are positioned close to each other in this space.

How Does Word2Vec Work?

Word2Vec employs two primary algorithms:

• Continuous Bag of Words (CBOW): This algorithm predicts a word based on its surrounding context.
• Skip-gram: This algorithm predicts the context based on a given word.

Image Source: Kavita Ganesan

Both methods result in dense vector representations of words. Each word is associated with a unique vector in a high-dimensional space, where the distance between vectors reflects the semantic similarity of the words they represent.

Real-World Example

Consider a book recommendation system for a bookstore. By analyzing book descriptions with Word2Vec, the system can recommend books that are semantically similar. For instance, if a user shows interest in "space exploration," the system might suggest books related to "astronomy" or "NASA missions," even if these terms are not explicitly mentioned in the user's search.

GloVe (Global Vectors for Word Representation)

What is GloVe?

GloVe is another word representation technique that learns word vectors from global word-word co-occurrence statistics in a corpus. Unlike Word2Vec, which focuses on local context, GloVe captures both local and global statistical information about words.

How Does GloVe Work?

GloVe constructs a co-occurrence matrix that records how frequently pairs of words appear together in a text corpus. It then trains a model to learn word vectors that can predict these co-occurrence probabilities. The resulting vectors encode rich semantic relationships and can capture analogies, such as "king" - "man" + "woman" = "queen."

Example of Visualizing GloVe Word Embeddings

GloVe is often used in NLP tasks like sentiment analysis, where understanding the context of words is crucial. For example, the word "bank" could mean a financial institution or the side of a river. GloVe helps disambiguate such words based on the context in which they appear.

By leveraging these word representation techniques, including Word2Vec and GloVe, you can enhance the performance of your NLP models.