Abstract/Details

Learning and inference in weighted logic with application to natural language processing


2008 2008

Other formats: Order a copy

Abstract (summary)

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.

Indexing (details)


Subject
Computer science
Classification
0984: Computer science
Identifier / keyword
Applied sciences; Language processing; Learning; Natural language processing; Weighted logic
Title
Learning and inference in weighted logic with application to natural language processing
Author
Culotta, Aron
Number of pages
129
Publication year
2008
Degree date
2008
School code
0118
Source
DAI-B 69/09, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549789444
Advisor
McCallum, Andrew
University/institution
University of Massachusetts Amherst
Department
Computer Science
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3325268
ProQuest document ID
304566960
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304566960
Access the complete full text

You can get the full text of this document if it is part of your institution's ProQuest subscription.

Try one of the following:

  • Connect to ProQuest through your library network and search for the document from there.
  • Request the document from your library.
  • Go to the ProQuest login page and enter a ProQuest or My Research username / password.