Learning and inference in weighted logic with application to natural language processing
Aron Culotta, PhD Thesis (UMass), 2008
Abstract
Over the past two decades, statistical machine learning approaches to natural
language processing have largely replaced earlier logic-based systems. These
probabilistic methods have proven to be well-suited to the ambiguity inherent
in human communication. However, the shift to statistical modeling has mostly
abandoned the representational advantages of logic-based approaches. For
example, many language processing problems can be more meaningfully expressed
in first-order logic rather than propositional logic. Unfortunately, most
machine learning algorithms have been developed for propositional knowledge
representations.
In recent years, there have been a number of attempts to
combine logical and probabilistic approaches to artificial
intelligence. However, their impact on real-world applications has been
limited because of serious scalability issues that arise when algorithms
designed for propositional representations are applied to first-order logic
representations. In this thesis, we explore approximate learning and inference
algorithms that are tailored for higher-order representations, and demonstrate
that this synthesis of probability and logic can significantly improve the
accuracy of several language processing systems.
Citation
@phdthesis{culotta08learning,
author = {Aron Culotta},
title = {Learning and inference in weighted logic with application to natural language processing},
school = {University of Massachusetts},
year = {2008},
month = {May},
}