Word embedding is a key technique in natural language processing (NLP) that allows computers to understand and represent the meaning of words. This tutorial delves deep into the concepts and implementations of word embeddings.
What is Word Embedding?
Word embedding is a method of representing words in a numerical vector form, where each dimension of the vector corresponds to a feature that helps capture the word's meaning. This representation allows machines to perform various NLP tasks more effectively.
Types of Word Embeddings
- Word2Vec: This is one of the most popular methods for creating word embeddings. It uses either the Continuous Bag-of-Words (CBOW) or Skip-Gram architecture to predict the context of a word given its surrounding words.
- GloVe: Global Vectors for Word Representation is another widely used method that learns word vectors based on the global statistical properties of word co-occurrence.
How Word Embeddings Work
Word embeddings work by mapping words to vectors in a multi-dimensional space. Similar words will be close to each other in this space, and dissimilar words will be farther apart.
Key Concepts
- Semantic Similarity: Words that are semantically similar will have vectors that are close together.
- Word Analogies: Word embeddings can be used to solve word analogy problems, such as "man is to king as woman is to what?" The answer to this question can be found by calculating the vector difference between "man - king" and "woman - ?".
Applications
Word embeddings have a wide range of applications in NLP, including:
- Text Classification
- Sentiment Analysis
- Machine Translation
- Named Entity Recognition
Further Reading
For a more comprehensive understanding of word embeddings, check out our Introduction to Word Embeddings.