# A Systematic Comparison of English Noun Compound Representations

## TL;DR

This paper systematically compares different noun compound representation methods, finding that compositional functions generally outperform distributional ones and that combining approaches could yield better results.

## Contribution

It provides a comprehensive comparison of noun compound representations, highlighting the effectiveness of composition functions and suggesting joint training for improved performance.

## Key findings

- Composition functions outperform distributional representations in most cases.
- Representation quality improves with increased computational power.
- No single function is best for all scenarios, indicating potential for joint training.

## Abstract

Building meaningful representations of noun compounds is not trivial since many of them scarcely appear in the corpus. To that end, composition functions approximate the distributional representation of a noun compound by combining its constituent distributional vectors. In the more general case, phrase embeddings have been trained by minimizing the distance between the vectors representing paraphrases. We compare various types of noun compound representations, including distributional, compositional, and paraphrase-based representations, through a series of tasks and analyses, and with an extensive number of underlying word embeddings. We find that indeed, in most cases, composition functions produce higher quality representations than distributional ones, and they improve with computational power. No single function performs best in all scenarios, suggesting that a joint training objective may produce improved representations.