# Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting

## TL;DR

This paper investigates the challenges of classifying multiple relations in ConceptNet for natural language understanding, highlighting issues like relation ambiguity and argument diversity, and proposes a multi-label neural classification approach.

## Contribution

It introduces a neural multi-label classification method tailored for ConceptNet relations and analyzes the impact of relation properties on classification difficulty.

## Key findings

- Relation ambiguity significantly affects classification accuracy.
- Argument diversity poses major challenges for relation learning.
- Customized evaluation methods help address resource incompleteness.

## Abstract

Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in CONCEPTNET, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the CONCEPTNET resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.