Using Answer Set Programming in an Inference-Based approach to Natural Language Semantics
Farid Nouioua (LIPN), Pascal Nicolas (LERIA)
TL;DR
This paper explores the application of Answer Set Programming within an inference-based framework to improve the modeling and understanding of natural language semantics.
Contribution
It introduces a novel method integrating Answer Set Programming into inference-based semantic analysis for natural language processing.
Findings
Demonstrates effective semantic inference using ASP
Improves accuracy of semantic parsing
Provides a new framework for semantic analysis
Abstract
Using Answer Set Programming in an Inference-Based approach to Natural Language Semantics
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Multi-Agent Systems and Negotiation · Bayesian Modeling and Causal Inference