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

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10407 - 10420 December 7-11, 2022 ©2022 Association for Computational Linguistics “Covid vaccine is against Covid but Oxford vaccine is made at Oxford!” Semantic Interpretation of Proper Noun Compounds Keshav Kolluru† Gabriel Stanovsky‡ Mausam† † Indian Institute of Technology Delhi keshav.kolluru@gmail.com, mausam@cse.iitd.ac.in ‡ The Hebrew University of Jerusalem gabriel.stanovsky@mail.huji.ac.il Abstract Proper noun compounds, e.g., “Covid vac- cine”, convey information in a succinct manner (a “Covid vaccine” is a “vaccine that immu- nizes 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 an- notated 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 au- tomatically generating the semantic interpre- tations from proper noun compounds, ranging from few-shot prompting to supervised learn- ing, with varying degrees of knowledge about the constituent nouns. We find that adding tar- geted knowledge, particularly about the com- mon noun, results in performance gains of upto 2.8%. Finally, we integrate our model gener- ated 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. 1 Introduction Proper noun compounds (PNCs) (Breban et al., 2019)1 are grammatical constructions where a proper noun is followed by a common noun, for ex- ample: Covid vaccines or Buddhist monks. These often serve as a compact way to convey information about an already known entity, omitting predicates that are interpreted by the reader using surround- ing context, common sense, and world knowledge. For example, a reader is likely to interpret that “Buddhist monks” are “religious people who are buddhists”. In other cases, PNCs are used to iden- tify specific entities, and do not provide additional 1also referred to as proper noun modified compounds. information. For example, Watergate scandal and Kawasaki disease do not have any implicit relation between the proper and common noun as they refer to a specific instance of a scandal and a disease. Table 1 provides additional examples. Thanks to their brevity, PNCs are commonly used to shorten descriptions in space-constrained domains, such as news articles headlines (Breban et al., 2019). However, we find that prior work on compound noun interpretations only considered cases where the constituents are common nouns (e.g. baby oil), thus missing all of the information conveyed in proper noun compounds (Shwartz and Waterson, 2018; Hendrickx et al., 2013). To address this limitation in current systems, we begin by defining the task of PNC interpretation as two subsequent stages (Section 3). The first stage requires identifying whether a given PNC is compositional or not, while the second stage is the generation of an interpretation, where applicable. In Section 4, we …

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