What does "Word Embedding" mean?

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.

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