GloVe embeddings in NLP - Model Metrics & Evaluation

Metrics & Evaluation - GloVe embeddings

Which metric matters for GloVe embeddings and WHY

GloVe embeddings create word vectors that capture meaning by looking at word co-occurrence in text. To check if these vectors are good, we use cosine similarity. This measures how close two word vectors are in meaning. A higher cosine similarity means words are more related. For example, "king" and "queen" should have a high similarity.

We also use analogy tests like "king - man + woman = ?" to see if the embeddings capture relationships. These tests show if the model understands word connections beyond just frequency.

Confusion matrix or equivalent visualization

Since GloVe embeddings are not classifiers, we don't use confusion matrices. Instead, we look at similarity scores between word pairs.

    Word Pair        Cosine Similarity
    ----------------------------------
    (king, queen)          0.78
    (king, man)            0.75
    (king, apple)          0.12
    (apple, fruit)         0.82
    (apple, car)           0.10
    

High similarity for related words and low for unrelated words shows good embeddings.

Precision vs Recall tradeoff with concrete examples

For GloVe embeddings, the tradeoff is between specificity and generalization.

• High specificity: Embeddings capture very detailed word meanings but may miss broader connections. This is like remembering exact details but missing the big picture.
• High generalization: Embeddings capture broad relationships but may confuse similar words. This is like understanding the theme but mixing up characters.

Choosing the right balance depends on the task. For example, in sentiment analysis, generalization helps group similar feelings. In translation, specificity helps pick exact words.

What "good" vs "bad" metric values look like for GloVe embeddings

Good embeddings:

• Cosine similarity close to 1 for related words (e.g., > 0.7 for synonyms or related concepts)
• Cosine similarity close to 0 or negative for unrelated words
• Analogy test accuracy above 70% on standard benchmarks

Bad embeddings:

• Cosine similarity high for unrelated words (e.g., > 0.5 for random pairs)
• Low accuracy on analogy tests (below 40%)
• Vectors that do not cluster similar words together

Metrics pitfalls

• Ignoring context: GloVe embeddings are static and do not change with sentence meaning, so similarity may be misleading for words with multiple meanings.
• Overfitting to frequent words: Very common words may dominate co-occurrence counts, skewing embeddings.
• Using cosine similarity alone: It does not capture all semantic nuances; relying only on it can miss problems.
• Not testing on analogy or downstream tasks: Embeddings may look good by similarity but fail in real tasks like classification or translation.

Self-check question

Your GloVe embeddings show cosine similarity of 0.85 for "king" and "queen", but only 0.3 for "apple" and "fruit". Is this good? Why or why not?

Answer: This is not ideal. "King" and "queen" are related, so 0.85 is good. But "apple" and "fruit" are also related and should have a high similarity. A low 0.3 suggests the embeddings do not capture this relationship well. You may need to retrain or check your data.

Key Result

Cosine similarity and analogy test accuracy are key to evaluating GloVe embeddings' quality.