Difference between revisions of "Daume et al, ML 2009"
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[[File:searn-algorithm.png]] | [[File:searn-algorithm.png]] | ||
| − | The key points | + | 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 [[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* '''SEARN in Practice''': This unpublished manuscript showcases three example problems where SEARN can be used - [[RelatedPaper::Daume_et_al,_2006]]. | * '''SEARN in Practice''': This unpublished manuscript showcases three example problems where SEARN can be used - [[RelatedPaper::Daume_et_al,_2006]]. | ||
Revision as of 15:26, 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. 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- SEARN in Practice: This unpublished manuscript showcases three example problems where SEARN can be used - Daume_et_al,_2006.
