To learn frame embeddings, replace a focus verb in the training data with its corresponding FrameNet frame label (either provided in gold-standard data or tagged via a semantic parser). Then, train word embedding models (e.g., word2vec skip-gram, GloVe, and FastText) on this modified data. This process yields a joint embedding space containing both common word embeddings and FrameNet frame embeddings.

The quality of these frame embeddings can be evaluated using two metrics:

1. Lexical Metric (lex): Evaluates whether a frame embedding is similar to words within its frame and dissimilar to those outside it: $lex(f) = \sum_{w\in f}\frac{cos(E_w, E_f)}{|f|} - \sum_{w\notin f}^{k}\frac{cos(E_w, E_f)}{|k|}$

2. Structural Similarity Metric (str): Evaluates whether frame embeddings reflect the hierarchy of connected frames in FrameNet by checking similarity around their neighbors: $str(f) = \sum_{n\in N}\frac{cos(E_n, E_f)}{|N|} - \sum_{n\notin N}^{k}\frac{cos(E_n, E_f)}{|k|}$