Sense disambiguation of compound constituents

TL;DR

This paper introduces a novel set expansion method for disambiguating the meanings of constituents in noun-noun compounds, improving semantic understanding by addressing polysemy in distributional semantics.

Contribution

It adapts a set expansion approach to the problem of sense disambiguation of compound constituents, demonstrating its effectiveness on a large dataset.

Findings

Method shows success in disambiguating compound constituents.

Performance depends on compound frequency in the dataset.

Approach enhances semantic vector representations for compounds.

Abstract

In distributional semantic accounts of the meaning of noun-noun compounds (e.g. starfish, bank account, houseboat) the important role of constituent polysemy remains largely unaddressed(cf. the meaning of star in starfish vs. star cluster vs. star athlete). Instead of semantic vectors that average over the different meanings of a constituent, disambiguated vectors of the constituents would be needed in order to see what these more specific constituent meanings contribute to the meaning of the compound as a whole. This paper presents a novel approach to this specific problem of word sense disambiguation: set expansion. We build on the approach developed by Mahabal et al. (2018) which was originally designed to solve the analogy problem. We modified their method in such a way that it can address the problem of sense disambiguation of compound constituents. The results of experiments with a data set of almost 9000 compounds (LADEC, Gagn\'e et al. 2019) suggest that this approach is successful, yet the success is sensitive to the frequency with which the compounds are attested.