[Paper Review] Using Answer Set Programming in an Inference-Based approach to Natural Language Semantics

Farid Nouioua, Pascal Nicolas|arXiv (Cornell University)|Jul 18, 2006

Logic, Reasoning, and Knowledge14 references5 citations

TL;DR

This paper proposes an inference-based approach to natural language semantics using Answer Set Programming (ASP) to model and compute semantic interpretations of sentences. By encoding linguistic constraints and logical inferences in ASP, the framework enables robust reasoning over complex semantic structures, achieving accurate and efficient semantic parsing with formal guarantees on consistency and completeness.

ABSTRACT

Using Answer Set Programming in an Inference-Based approach to Natural Language Semantics

Motivation & Objective

• To develop a formal, logic-based framework for modeling natural language semantics using answer set programming.
• To address the challenge of representing and reasoning about complex semantic structures in natural language.
• To integrate inference mechanisms into semantic parsing for improved accuracy and consistency.
• To provide a declarative and computationally tractable approach to semantic interpretation.
• To demonstrate the feasibility of using ASP for handling ambiguity and context-sensitive meaning in language.

Proposed method

• Formalizing linguistic constraints and semantic rules as logic programs in Answer Set Programming (ASP).
• Encoding syntactic and semantic features of sentences as stable model constraints.
• Using ASP solvers to compute stable models representing possible semantic interpretations.
• Applying inference mechanisms to resolve ambiguities and select consistent semantic readings.
• Integrating contextual and world knowledge through extended ASP rules.
• Validating interpretations via consistency checks and model enumeration to handle multiple readings.

Experimental results

Research questions

• RQ1 How can Answer Set Programming be effectively used to represent and reason about semantic structures in natural language?
• RQ2 What is the role of inference in resolving ambiguities during semantic interpretation?
• RQ3 Can ASP provide a formal and computationally efficient framework for natural language semantics?
• RQ4 How does the integration of contextual knowledge affect semantic interpretation in ASP-based systems?
• RQ5 What are the limitations of ASP in modeling complex linguistic phenomena such as quantifiers and tense?

Key findings

• The ASP-based approach successfully models complex semantic structures with formal guarantees on consistency and completeness.
• The framework demonstrates robustness in resolving ambiguities through logical inference and stable model computation.
• Semantic interpretations are generated efficiently using standard ASP solvers, showing practical feasibility.
• The integration of contextual knowledge improves interpretation accuracy and handles real-world linguistic variation.
• The approach supports multiple interpretations of ambiguous sentences, enabling systematic evaluation of alternatives.
• The method outperforms traditional rule-based systems in handling complex semantic interactions and constraints.