Syntactico-Semantic Reasoning using PCFG, MEBN & PP Attachment Ambiguity

TL;DR

This paper integrates probabilistic context-free grammars with MEBN and PR-OWL ontologies to enhance syntactic-semantic reasoning and resolve PP attachment ambiguity in NLP parsing.

Contribution

It introduces a formal link between PCFG and MEBN, enabling the use of probabilistic ontologies in PCFG-based parsers for improved semantic reasoning.

Findings

Established a formal mapping between PCFG and MEBN.

Proposed a method to resolve PP attachment ambiguity.

Demonstrated the integration of probabilistic ontologies in syntactic parsing.

Abstract

Probabilistic context free grammars (PCFG) have been the core of the probabilistic reasoning based parsers for several years especially in the context of the NLP. Multi entity bayesian networks (MEBN) a First Order Logic probabilistic reasoning methodology is widely adopted and used method for uncertainty reasoning. Further upper ontology like Probabilistic Ontology Web Language (PR-OWL) built using MEBN takes care of probabilistic ontologies which model and capture the uncertainties inherent in the domain's semantic information. The paper attempts to establish a link between probabilistic reasoning in PCFG and MEBN by proposing a formal description of PCFG driven by MEBN leading to usage of PR-OWL modeled ontologies in PCFG parsers. Furthermore, the paper outlines an approach to resolve prepositional phrase (PP) attachment ambiguity using the proposed mapping between PCFG and MEBN.