Word Embedding is a technique in natural language processing where words or phrases from a vocabulary are mapped to vectors of real numbers. Understanding this term helps in comprehending the semantic relationships within the data
Use Cases
Semantic Similarity
Measuring similarity between words based on vector distances.
Text Classification
Representing words as vectors for training machine learning models.
Language Translation
Mapping words across languages to improve translation accuracy.
Importance
Semantic Representation
Captures semantic meaning and relationships between words.
Feature Extraction
Provides dense and meaningful representations for downstream tasks.
Computational Efficiency
Reduces the computational complexity of handling large vocabularies in text processing.
Analogies
Word Embedding is like assigning coordinates to words on a map based on their meanings and contexts. Just as nearby words on the map have similar meanings or relationships, word embeddings represent words as vectors in a space where similar words are closer together.
