Vector_Embedding.py

Explanation of the Script

1. Vocabulary and Embedding Layer:

   • Terms are mapped to indices using a dictionary.
   • The embedding layer learns continuous vector representations for these terms.

2. Cosine Similarity:

   • The cosine similarity function measures how similar two terms are in the embedding space. Higher values indicate closer relationships.

3. Visualization:

   • Embeddings are plotted in a 2D space to show semantic relationships. Terms with similar meanings (e.g., “king” and “queen”) are expected to cluster together.

4. t-SNE for Dimensionality Reduction:

   • If the embedding dimension is higher than 2, t-SNE can reduce it to 2D for visualization while preserving semantic relationships.

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Outputs

1. Cosine Similarities:

   • Pairwise similarity scores between terms to quantify their semantic closeness.

2. Visualization:

   • A scatter plot showing the positions of terms in the embedding space.