Evaluate Embedding Methods

How to Evaluate Embedding Methods

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).

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.

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.

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?