To evaluate embeddings, you can use intrinsic or extrinsic methods, which differ in their focus and application. Intrinsic evaluation directly measures how well embeddings capture linguistic or semantic relationships, often using controlled tasks. Extrinsic evaluation tests embeddings in real-world applications to see if they improve performance on downstream tasks like classification or recommendation. Both approaches are complementary, but they answer different questions: intrinsic evaluation focuses on the quality of the embeddings themselves, while extrinsic evaluation assesses their practical utility.
For intrinsic evaluation, common methods include word similarity tasks, analogy tests, and clustering analysis. For example, in word similarity tasks, you might use datasets like WordSim-353, where embeddings are scored based on how closely their cosine similarity matches human-rated similarity scores (e.g., "dog" and "puppy" should have high similarity). Analogy tasks, like "king - man + woman = queen," test if embeddings preserve relational patterns. Tools like Gensim or scikit-learn can calculate cosine similarity or perform dimensionality reduction (e.g., t-SNE) to visualize clustering. However, intrinsic methods have limitations: they assume predefined relationships and may not reflect how embeddings perform in real applications.
Extrinsic evaluation involves integrating embeddings into a larger system and measuring task-specific metrics. For instance, if you’re using word embeddings for sentiment analysis, you could train a classifier (e.g., a neural network or logistic regression model) on the embeddings and compare its accuracy/F1-score to a baseline (e.g., TF-IDF features). In recommendation systems, you might test if item embeddings improve click-through rates. Extrinsic evaluation is context-dependent: BERT embeddings might excel in NLP tasks like named entity recognition but add little value in a simple spam detection model. The downside is that it’s time-consuming, as it requires building and tuning full pipelines. A practical approach is to start with intrinsic evaluation for quick validation, then use extrinsic tests for final validation in your target application. For example, evaluate GloVe embeddings intrinsically using analogy tests, then extrinsically by plugging them into a text classifier and comparing performance metrics like precision or recall.