  We present the New York Times Word Innovation Types dataset, or NYTWIT, a
collection of over 2,500 novel English words published in the New York Times
between November 2017 and March 2019, manually annotated for their class of
novelty (such as lexical derivation, dialectal variation, blending, or
compounding). We present baseline results for both uncontextual and contextual
prediction of novelty class, showing that there is room for improvement even
for state-of-the-art NLP systems. We hope this resource will prove useful for
linguists and NLP practitioners by providing a real-world environment of novel
word appearance.
