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 this https URL.

通过各种神经模型，尤其是针对基本名词的共同知识，我们可以自动生成专有名词复合词的语义解释。将这些解释与现有的 Open IE 系统集成，可以获得更高的收益率和更高的准确性。