A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection
This addresses the challenge of compositionality detection for natural language processing researchers, offering an unsupervised alternative to existing supervised methods.
The paper tackled the problem of detecting compositionality in language by proposing an unsupervised method that represents phrases as ranked lists and uses distance and correlation metrics to approximate semantic similarity, achieving superior performance compared to supervised baselines on a dataset of 1048 human-annotated phrases.
Compositionality in language refers to how much the meaning of some phrase can be decomposed into the meaning of its constituents and the way these constituents are combined. Based on the premise that substitution by synonyms is meaning-preserving, compositionality can be approximated as the semantic similarity between a phrase and a version of that phrase where words have been replaced by their synonyms. Different ways of representing such phrases exist (e.g., vectors [1] or language models [2]), and the choice of representation affects the measurement of semantic similarity. We propose a new compositionality detection method that represents phrases as ranked lists of term weights. Our method approximates the semantic similarity between two ranked list representations using a range of well-known distance and correlation metrics. In contrast to most state-of-the-art approaches in compositionality detection, our method is completely unsupervised. Experiments with a publicly available dataset of 1048 human-annotated phrases shows that, compared to strong supervised baselines, our approach provides superior measurement of compositionality using any of the distance and correlation metrics considered.