  Proper noun compounds, e.g., "Covid vaccine", convey information in a
succinct manner (a "Covid vaccine" is a "vaccine that immunizes against the
Covid disease"). These are commonly used in short-form domains, such as news
headlines, but are largely ignored in information-seeking applications. To
address this limitation, we release a new manually annotated dataset, ProNCI,
consisting of 22.5K proper noun compounds along with their free-form semantic
interpretations. ProNCI is 60 times larger than prior noun compound datasets
and also includes non-compositional examples, which have not been previously
explored. We experiment with various neural models for automatically generating
the semantic interpretations from proper noun compounds, ranging from few-shot
prompting to supervised learning, with varying degrees of knowledge about the
constituent nouns. We find that adding targeted knowledge, particularly about
the common noun, results in performance gains of upto 2.8%. Finally, we
integrate our model generated interpretations with an existing Open IE system
and observe an 7.5% increase in yield at a precision of 85%. The dataset and
code are available at https://github.com/dair-iitd/pronci.
