Syntactico-Semantic Reasoning using PCFG, MEBN & PP Attachment Ambiguity
This work addresses a specific challenge in NLP parsing for researchers, but it appears incremental as it combines existing methods without major breakthroughs.
The paper tackles the problem of linking probabilistic reasoning in PCFG and MEBN to enhance NLP parsers, proposing a formal mapping that enables the use of PR-OWL ontologies in PCFG and applies this to resolve PP attachment ambiguity.
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.