Word2Vec.py

The script can benefit from Word2Vec embeddings by replacing the randomly initialized embeddings with pretrained or trained embeddings generated using Word2Vec. These embeddings provide a meaningful semantic structure that is learned from a corpus of text, enhancing the visualization and cosine similarity calculations.

Benefits:

1. Meaningful Relationships: Words like “king” and “queen” will naturally be closer than “king” and “apple.”
2. Analogy Solving: Word2Vec supports vector arithmetic to solve word analogies (e.g., “man is to king as woman is to queen”).
3. Improved Visualizations: The embeddings reflect real-world semantic and syntactic relationships, making the 2D plots more interpretable.

Further Enhancements

1. Train Your Word2Vec:
   • Train embeddings on a custom corpus using gensim.models.[[Word2Vec]] to reflect domain-specific semantics.
2. Hybrid Embeddings:
   • Combine Word2Vec with other models (e.g., BERT or Sentence Transformers) for tasks requiring contextual understanding.

Using glove-wiki-gigaword-100:

• A GloVe model with 100-dimensional embeddings trained on the Wikipedia Gigaword dataset.
• Approximate size: ~100MB.

Expected Outcome

1. Visualization:
   • Terms from the same category (e.g., royalty, fruits, animals) will cluster together in the t-SNE plot.
2. Cosine Similarity:
   • Similar terms (e.g., “king” and “queen” or “apple” and “orange”) will have higher cosine similarity scores.
3. Semantic Diversity:
   • The expanded list increases the diversity of semantic relationships and highlights the strength of embeddings in grouping similar concepts.