Difference between revisions of "Daume et al, ML 2009"
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== Summary == | == Summary == | ||
| − | This journal [[Category::paper]] introduces [[UsesMethod::SEARN]], a meta-algorithm that combines searching and learning to make structured predictions. The algorithm is summarized in the following figure, from page 6 of the paper: | + | This journal [[Category::paper]] introduces [[UsesMethod::SEARN]], a meta-algorithm that combines searching and learning to make structured predictions. Note that this is the journal version of the 2006 paper that introduced this method. |
| + | |||
| + | The algorithm is summarized in the following figure, from page 6 of the paper: | ||
[[File:searn-algorithm.png]] | [[File:searn-algorithm.png]] | ||
The key points from the paper are: | The key points from the paper are: | ||
| − | * SEARN is an algorithm that solves complex structured predictions with minimal assumptions on the structure of the output and loss function | + | * [[UsesMethod::SEARN]] is an algorithm that solves complex structured predictions with minimal assumptions on the structure of the output and loss function |
| − | * Their experiments show that SEARN is competitive with existing standard structured prediction algorithms on sequence labeling tasks. | + | * Their experiments show that [[UsesMethod::SEARN]] is competitive with existing standard structured prediction algorithms on sequence labeling tasks. |
| − | * Authors described the use of SEARN on [[AddressesProblem::text summarization]], which yielded state-of-the-art performance. | + | * Authors described the use of [[UsesMethod::SEARN]] on [[AddressesProblem::text summarization]], which yielded state-of-the-art performance. |
== Related papers == | == Related papers == | ||
* '''SEARN in Practice''': This unpublished manuscript showcases three example problems where SEARN can be used - [[RelatedPaper::Daume_et_al,_2006]]. | * '''SEARN in Practice''': This unpublished manuscript showcases three example problems where SEARN can be used - [[RelatedPaper::Daume_et_al,_2006]]. | ||
Latest revision as of 18:22, 30 September 2010
Citation
Duame, H., Langford, J., and Marcu, D. 2009. Search-based structured prediction. Machine Learning. 75. 3. p297-325
Online version
Search-based structured prediction
Summary
This journal paper introduces SEARN, a meta-algorithm that combines searching and learning to make structured predictions. Note that this is the journal version of the 2006 paper that introduced this method.
The algorithm is summarized in the following figure, from page 6 of the paper:
The key points from the paper are:
- SEARN is an algorithm that solves complex structured predictions with minimal assumptions on the structure of the output and loss function
- Their experiments show that SEARN is competitive with existing standard structured prediction algorithms on sequence labeling tasks.
- Authors described the use of SEARN on text summarization, which yielded state-of-the-art performance.
Related papers
- SEARN in Practice: This unpublished manuscript showcases three example problems where SEARN can be used - Daume_et_al,_2006.
