# A Multivariate Model for Representing Semantic Non-compositionality

## TL;DR

This paper introduces a multivariate model that effectively identifies semantically non-compositional phrases by integrating multiple characteristics, outperforming previous models that focus on single features.

## Contribution

The paper presents a novel multivariate model that combines various characteristics to improve the identification of non-compositional phrases in NLP.

## Key findings

- Model outperforms existing approaches
- Integrates multiple characteristics for better accuracy
- Highlights importance of combined features in semantic analysis

## Abstract

Semantically non-compositional phrases constitute an intriguing research topic in Natural Language Processing. Semantic non-compositionality --the situation when the meaning of a phrase cannot be derived from the meaning of its components, is the main characteristic of such phrases, however, they bear other characteristics such as high statistical association and non-substitutability. In this work, we present a model for identifying non-compositional phrases that takes into account all of these characteristics. We show that the presented model remarkably outperforms the existing models of identifying non-compositional phrases that mostly focus only on one of these characteristics.

---
Source: https://tomesphere.com/paper/1908.05490