NYTWIT: A Dataset of Novel Words in the New York Times
This provides a resource for linguists and NLP practitioners to study novel word appearance in a real-world environment, but it is incremental as it focuses on dataset creation and baseline results.
The authors tackled the problem of identifying novel English words in real-world text by creating NYTWIT, a dataset of over 2,500 annotated novel words from the New York Times, and showed that current NLP systems have room for improvement in predicting novelty classes.
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.