The intuition of FEMA is similar to SCL and other prior work: it relies on co-occurrence statistics to link features across domains.
fEMA exploits the tendency of many NLP tasks to divide features into templates, and induces feature embeddings by using the features in each template to predict the active features in all other templates -just as the skipgram model learns word embeddings to predict neighboring words.