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

"Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds