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When errors become the rule: A survey of Transformation-Based Learning

Uneson, Marcus LU (2011) In LU-DV-EX DATM01 20111
Computer Science
Abstract
Transformation-based learning (TBL) is a machine learning method for sequential classification, invented by Eric Brill (1993, 1995). It is widely used within
natural language processing (but surprisingly little in other areas).

TBL is a simple yet flexible paradigm, which achieves competitive or even state-of-the-art performance in several areas and does not overtrain easily. It is especially successful at catching local, fixed-distance dependencies. The learned representation -- an ordered list of transformation rules -- is compact and efficient, with clear, declarative semantics. Individual rules are interpretable and often meaningful to humans.

The present thesis has two main parts. First and foremost, we offer a survey of the... (More)
Transformation-based learning (TBL) is a machine learning method for sequential classification, invented by Eric Brill (1993, 1995). It is widely used within
natural language processing (but surprisingly little in other areas).

TBL is a simple yet flexible paradigm, which achieves competitive or even state-of-the-art performance in several areas and does not overtrain easily. It is especially successful at catching local, fixed-distance dependencies. The learned representation -- an ordered list of transformation rules -- is compact and efficient, with clear, declarative semantics. Individual rules are interpretable and often meaningful to humans.

The present thesis has two main parts. First and foremost, we offer a survey of the most important theoretical work on TBL. It is intended to be informal but relatively comprehensive, addressing a perceived gap in the literature. Second, in a more practical part, we describe a recursive, parallelizable rephrasing, well suited for declarative languages, of a fast imperative learning algorithm proposed by Ngai and Florian (2001). We implement and test this algorithm in the functional language Haskell. (Less)
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author
Uneson, Marcus LU
supervisor
organization
course
DATM01 20111
year
type
M1 - University Diploma
subject
keywords
computer science, computational linguistics, machine learning, sequential classification, brill tagging, transformation-based learning
publication/series
LU-DV-EX
report number
2011-157
ISSN
1651-6389
language
English
id
2007414
date added to LUP
2011-12-08 11:16:29
date last changed
2011-12-08 11:16:29
@misc{2007414,
  abstract     = {Transformation-based learning (TBL) is a machine learning method for sequential classification,  invented by Eric Brill (1993, 1995). It is widely used within
natural language processing (but surprisingly little in other areas).

TBL is a simple yet flexible paradigm, which achieves competitive or even state-of-the-art performance in several areas and does not overtrain easily. It is especially successful at catching local, fixed-distance dependencies. The learned representation -- an ordered list of transformation rules -- is compact and efficient, with clear, declarative semantics. Individual rules are interpretable and often meaningful to humans.

The present thesis has two main parts. First and foremost, we offer a survey of the most important theoretical work on TBL. It is intended to be informal but relatively comprehensive, addressing a perceived gap in the literature. Second, in a more practical part, we describe a recursive, parallelizable rephrasing, well suited for declarative languages, of a fast imperative learning algorithm proposed by Ngai and Florian (2001). We implement and test this algorithm in the functional language Haskell.},
  author       = {Uneson, Marcus},
  issn         = {1651-6389},
  keyword      = {computer science,computational linguistics,machine learning,sequential classification,brill tagging,transformation-based learning},
  language     = {eng},
  note         = {Student Paper},
  series       = {LU-DV-EX},
  title        = {When errors become the rule: A survey of Transformation-Based Learning},
  year         = {2011},
}