Difference between revisions of "Tackstrom and McDonald, ECIR 2011. Discovering fine-grained sentiment with latent variable structured prediction models"
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== Summary == | == Summary == | ||
| − | This [[Category::paper]] investigates the use of latent variable structured prediction models for fine-grained sentiment analysis in the common situation where only coarse-grained supervision is available. The authors show how sentence level sentiment labels can be effectively learned from document-level supervision using [[UsesMethod::hidden conditional random fields]] (HCRFs) | + | This [[Category::paper]] investigates the use of latent variable structured prediction models for fine-grained sentiment analysis in the common situation where only coarse-grained supervision is available. The authors show how sentence level sentiment labels can be effectively learned from document-level supervision using [[UsesMethod::hidden conditional random fields]] (HCRFs). The authors show improvements over both lexicon and existing machine learning based approaches. |
== Method == | == Method == | ||
Revision as of 18:24, 28 November 2011
Contents
Citation
O. Tackstrom and R. McDonald. 2011. Discovering fine-grained sentiment with latent variable structured prediction models. In Proceedings of ECIR-2011, pp 764–773, Dublin, Ireland.
Online Version
Discovering fine-grained sentiment with latent variable structured prediction models
Summary
This paper investigates the use of latent variable structured prediction models for fine-grained sentiment analysis in the common situation where only coarse-grained supervision is available. The authors show how sentence level sentiment labels can be effectively learned from document-level supervision using hidden conditional random fields (HCRFs). The authors show improvements over both lexicon and existing machine learning based approaches.
