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
- Glossary > Letter: W
What does "Word Embedding" mean?

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.
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