Name
Abstract | ||
|---|---|---|
The increasingly popular use of as a resource to obtain labeled data has been contributing to the wide awareness of the machine learning community to the problem of supervised learning from multiple annotators. Several approaches have been proposed to deal with this issue, but they disregard sequence labeling problems. However, these are very common, for example, among the Natural Language Processing and Bioinformatics communities. In this paper, we present a probabilistic approach for sequence labeling using Conditional Random Fields (CRF) for situations where label sequences from multiple annotators are available but there is no actual ground truth. The approach uses the Expectation-Maximization algorithm to jointly learn the CRF model parameters, the reliability of the annotators and the estimated ground truth. When it comes to performance, the proposed method (CRF-MA) significantly outperforms typical approaches such as majority voting. |
Year | DOI | Venue |
|---|---|---|
2014 | https://doi.org/10.1007/s10994-013-5411-2 | Machine Learning |
Keywords | Field | DocType |
Multiple annotators,Crowdsourcing,Conditional random fields,Latent variable models,Expectation maximization | Conditional random field,Sequence labeling,Pattern recognition,Expectation–maximization algorithm,Crowdsourcing,Computer science,Supervised learning,Ground truth,Artificial intelligence,Probabilistic logic,Majority rule,Machine learning | Journal |
Volume | Issue | ISSN |
95 | 2 | 0885-6125 |
Citations | PageRank | References |
3 | 0.42 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filipe Rodrigues | 1 | 97 | 8.80 |
| Francisco C. Pereira | 2 | 331 | 33.07 |
| Bernardete Ribeiro | 3 | 758 | 82.07 |