Difference between revisions of "Brill, CL 1995"
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== Summary == | == Summary == | ||
| − | This | + | This [[Category::paper]] introduces a learning technique called "Transformation-based error-driven learning", a.k.a. [[UsesMethod::Transformation Based Learning]] (TBL). |
| − | The algorithm is | + | The key points from the paper are: |
| + | * [[UsesMethod::Transformation Based Learning]] is an algorithm that learns a sequence of transformations to improve tagging on some baseline tagger | ||
| + | * Transformations are broken down into two components: a ''triggering event'' (such as if the previous word is a determiner) and a ''re-write rule'' (such as change tag from modal to noun) | ||
| + | * Authors described the use of [[UsesMethod::Transformation Based Learning]] on [[AddressesProblem::POS Tagging]]. | ||
| + | |||
| + | |||
| + | == Transformation-Based Learning == | ||
| − | + | '''The learning algorithm is summarized as follows''': | |
| + | * | ||
| − | + | [[File:brill95_fig1.png]] | |
| − | + | ||
| − | * | + | '''Transformations are applied as follows''': |
| − | * | + | * Run initial-state annotator on unseen data |
| + | * Loop through ordered list of transformations, and apply each transformation. | ||
| + | |||
| + | |||
| + | == Transformation-based Learning for POS Tagging == | ||
| + | ... | ||
== Related papers == | == Related papers == | ||
* | * | ||
Revision as of 17:14, 31 October 2010
Contents
Citation
Brill, E. 1995. Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Computational Linguistics. 21. 4. p543-565
Online version
Summary
This paper introduces a learning technique called "Transformation-based error-driven learning", a.k.a. Transformation Based Learning (TBL).
The key points from the paper are:
- Transformation Based Learning is an algorithm that learns a sequence of transformations to improve tagging on some baseline tagger
- Transformations are broken down into two components: a triggering event (such as if the previous word is a determiner) and a re-write rule (such as change tag from modal to noun)
- Authors described the use of Transformation Based Learning on POS Tagging.
Transformation-Based Learning
The learning algorithm is summarized as follows:
Transformations are applied as follows:
- Run initial-state annotator on unseen data
- Loop through ordered list of transformations, and apply each transformation.
Transformation-based Learning for POS Tagging
...
