Composite Semantic Relation Classification

TL;DR

This paper introduces a novel approach for classifying complex semantic relations by combining a large knowledge base, navigational algorithms, and sequence classification, addressing limitations of previous lexical and distributional methods.

Contribution

It extends traditional semantic relation classification to handle composite relations using a hybrid model integrating knowledge bases and sequence algorithms.

Findings

Effective classification of composite semantic relations.

Outperforms traditional lexical and distributional models.

Provides a scalable solution for complex semantic tasks.

Abstract

Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification, extending the traditional semantic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem.