An Empirical Study on Compositionality in Compound Nouns

Siva Reddy, Diana McCarthy, Suresh Manandhar

International Joint Conference on Natural Language Processing · 2011 · 139 citations · 20 references

Concepts

TL;DR

A multiword is compositional if its meaning can be expressed in terms of the meaning of its constituents. The study collects and analyzes compositionality judgments for compound nouns via Mechanical Turk and uses the dataset to examine how constituent-level judgments relate to whole-phrase judgments. The authors compiled a dataset of constituent and whole-phrase compositionality judgments, then evaluated constituent-based and composition-function-based distributional models for compositionality detection. Both constituent-based and composition-function-based models perform competitively, with composition-function models slightly better, and additive models outperform multiplicative ones in both categories.

Abstract

A multiword is compositional if its meaning can be expressed in terms of the meaning of its constituents. In this paper, we collect and analyse the compositionality judgments for a range of compound nouns using Mechanical Turk. Unlike existing compositionality datasets, our dataset has judgments on the contribution of constituent words as well as judgments for the phrase as a whole. We use this dataset to study the relation between the judgments at constituent level to that for the whole phrase. We then evaluate two different types of distributional models for compositionality detection – constituent based models and composition function based models. Both the models show competitive performance though the composition function based models perform slightly better. In both types, additive models perform better than their multiplicative counterparts.

References

20