Representing Verbs as Argument Concepts

TL;DR

This paper introduces a framework for abstracting verb arguments into semantic concepts, enhancing natural language understanding by capturing fine-grained verb semantics.

Contribution

It proposes a novel method to automatically infer human-readable and machine-computable argument concepts for verbs, improving semantic representation.

Findings

High accuracy in inferring argument concepts

Effective abstraction of verb semantics

Enhanced natural language understanding

Abstract

Verbs play an important role in the understanding of natural language text. This paper studies the problem of abstracting the subject and object arguments of a verb into a set of noun concepts, known as the "argument concepts". This set of concepts, whose size is parameterized, represents the fine-grained semantics of a verb. For example, the object of "enjoy" can be abstracted into time, hobby and event, etc. We present a novel framework to automatically infer human readable and machine computable action concepts with high accuracy.