Representing Verbs as Argument Concepts
Yu Gong, Kaiqi Zhao, Kenny Q. Zhu

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.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Multimodal Machine Learning Applications
