Difference between revisions of "Brill, CL 1995"
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* 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) | * 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 [[AddressesProblem::POS Tagging]]. | * Authors described the use of Transformation Based Learning on [[AddressesProblem::POS Tagging]]. | ||
| − | + | * Some considerations: | |
| + | ** Constructing all possible transformations: manually create rules or make templates?, potentially huge search space can be problematic, may need to use linguistic intuition to limit space | ||
| + | ** No probabilities/confidence associated with results | ||
| + | ** Transformations in one environment could affect application in another: should transformations be applied immediately or only after entire corpus is examined for triggering conditions? what order do we process corpus? (left-to-right or right-to-left) | ||
== Transformation-Based Learning == | == Transformation-Based Learning == | ||
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[[File:brill95_fig1.png]] | [[File:brill95_fig1.png]] | ||
| + | |||
'''Transformations are applied as follows''': | '''Transformations are applied as follows''': | ||
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== Related papers == | == Related papers == | ||
| − | + | * '''SEARN in Practice''': This unpublished manuscript showcases three example problems where SEARN can be used - [[RelatedPaper::Daume_et_al,_2006]]. | |
| − | * | ||
Revision as of 17:23, 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.
- Some considerations:
- Constructing all possible transformations: manually create rules or make templates?, potentially huge search space can be problematic, may need to use linguistic intuition to limit space
- No probabilities/confidence associated with results
- Transformations in one environment could affect application in another: should transformations be applied immediately or only after entire corpus is examined for triggering conditions? what order do we process corpus? (left-to-right or right-to-left)
Transformation-Based Learning
The learning algorithm is summarized as follows (see Figure 1 from paper as well):
- Pass un-annotated corpus (training data) through initial-state annotator
- Compare against truth to get current score (based on number of classification errors)
- Loop until no transformation can be found to improve score (Greedy search)
- Consider all transformations rules applied to training data, select best
- Apply to transformation to data & get current score
- Add transformation to ordered transformation list
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
- SEARN in Practice: This unpublished manuscript showcases three example problems where SEARN can be used - Daume_et_al,_2006.
