The learning objective becomes: The hidden layer S of the autoencoder gives us synset embeddings.
the lexeme embeddings are defined when transitioning from W to S, or more explicitly by: there is also a second lexeme embedding in AutoExtend when transitioning form S to W : Aligning these two representations seems natural, so we impose the following lexeme constraints: this can also be expressed dimension-wise.