  Humans often make creative use of words to express novel senses. A
long-standing effort in natural language processing has been focusing on word
sense disambiguation (WSD), but little has been explored about how the sense
inventory of a word may be extended toward novel meanings. We present a
paradigm of word sense extension (WSE) that enables words to spawn new senses
toward novel context. We develop a framework that simulates novel word sense
extension by first partitioning a polysemous word type into two pseudo-tokens
that mark its different senses, and then inferring whether the meaning of a
pseudo-token can be extended to convey the sense denoted by the token
partitioned from the same word type. Our framework combines cognitive models of
chaining with a learning scheme that transforms a language model embedding
space to support various types of word sense extension. We evaluate our
framework against several competitive baselines and show that it is superior in
predicting plausible novel senses for over 7,500 English words. Furthermore, we
show that our WSE framework improves performance over a range of
transformer-based WSD models in predicting rare word senses with few or zero
mentions in the training data.
