Difference between revisions of "Choi, ACL 2010"
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== Summary == | == Summary == | ||
| − | This | + | This [[Category::paper]] apply a hierarchical parameter sharing technique using Conditional Random Field (CRF) to fine grained opinion analysis. It aim to jointly identify the boundaries of opinion expression as well as to determine two of their key attributes - polarity and intensity. |
== Brief description of the method == | == Brief description of the method == | ||
| + | They used following features: | ||
| + | |||
| + | * Prior-polarity & Prior-intensity | ||
| + | * EXP-Polarity, Exp-Intensity & EXP-Span: Expression level attributes | ||
| + | * Per-Token Features | ||
| + | ** Common Per-Token Features | ||
| + | ** Polarity Per-Token Features | ||
| + | ** Intensity Per-Token Features | ||
| + | * Transition Features | ||
| + | ** Polarity Transition Features | ||
| + | ** Intensity Transition Features | ||
== Experimental Result == | == Experimental Result == | ||
| − | + | The method was evaluated on the MultiPerspective Question Answering (MPQA) corpus. Joint with Hierarchy method works best while baseline which cascade two separately trained models performs worst. The performance for negative class is much higher than positive class. It is mainly because data for negative class is double than the positive class. | |
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Latest revision as of 18:26, 25 September 2011
Contents
Citation
Choi, Y., and C. Cardie. Hierarchical Sequential Learning for Extracting Opinions and their Attributes. ACL-2010
Online version
Summary
This paper apply a hierarchical parameter sharing technique using Conditional Random Field (CRF) to fine grained opinion analysis. It aim to jointly identify the boundaries of opinion expression as well as to determine two of their key attributes - polarity and intensity.
Brief description of the method
They used following features:
- Prior-polarity & Prior-intensity
- EXP-Polarity, Exp-Intensity & EXP-Span: Expression level attributes
- Per-Token Features
- Common Per-Token Features
- Polarity Per-Token Features
- Intensity Per-Token Features
- Transition Features
- Polarity Transition Features
- Intensity Transition Features
Experimental Result
The method was evaluated on the MultiPerspective Question Answering (MPQA) corpus. Joint with Hierarchy method works best while baseline which cascade two separately trained models performs worst. The performance for negative class is much higher than positive class. It is mainly because data for negative class is double than the positive class.
