Evaluate Embedding Methods

How to Evaluate Embedding Methods

1. Semantic Relationship Fidelity

A good embedding should place semantically similar sentences or words closer together in the embedding space. You can test this using:

a) Cosine Similarity

• Compute the cosine similarity between embedding vectors.

• Higher similarity between semantically related pairs (e.g., “Paris is the capital of France” vs “France’s capital is Paris”) indicates better embedding quality.

• Compare scores across methods:

  from sklearn.metrics.pairwise import cosine_similarity
  similarity = cosine_similarity([vec1], [vec2])

b) Analogy or Word Arithmetic

• Test whether embeddings support compositional reasoning.
• Example: $\text{Embedding}(\text{king})-\text{Embedding}(\text{man})+\text{Embedding}(\text{woman})\approx \text{queen}$
• This shows if semantic and syntactic dimensions are meaningfully encoded (syntactic relationships).

c) Clustering Consistency

• Cluster the embeddings (e.g. via k-means) and evaluate whether related texts group together.
• Measure cluster cohesion and separation (e.g. using Silhouette Score).

2. Information Content & Sparsity

a) Use TF-IDF as Baseline

• TF-IDF scores highlight the most important words in a text.
• Evaluate how well dense embeddings retain the importance structure identified by TF-IDF.
• For example, check whether high TF-IDF words receive higher attention in models like BERT (via attention weights) or influence sentence embedding directions.

3. Downstream Task Performance

Train simple classifiers (e.g. logistic regression) on embeddings to predict:

• Sentiment
• Topic
• Semantic similarity class (entailment, contradiction, etc.)

Better embeddings typically yield better accuracy/F1 on such tasks.

4. Visual Inspection

a) Dimensionality Reduction

• Use PCA, t-SNE, or UMAP to project embeddings into 2D.
• Visually inspect whether semantically similar items form coherent clusters.

Guiding Questions

• Do the embeddings distinguish fine-grained semantic shifts (e.g., “bank” as a financial institution vs riverbank)?
• Are word or sentence embeddings stable across paraphrased or reordered text?
• Do similar sentences result in embeddings with high cosine similarity?
• How well do embeddings handle OOV words or rare terms?