Name
Abstract | ||
|---|---|---|
We report on an active learning experiment for named entity recognition in the astronomy domain. Active learning has been shown to reduce the amount of labelled data required to train a supervised learner by selectively sampling more informative data points for human annotation. We inspect double annotation data from the same domain and quantify potential problems concerning annotators' performance. For data selectively sampled according to different selection metrics, we find lower inter-annotator agreement and higher per token annotation times. However, overall results confirm the utility of active learning. |
Year | Venue | Keywords |
|---|---|---|
2005 | CoNLL | informative data point,different selection metrics,human annotation,active learning experiment,active learning,labelled data,selective sampling,astronomy domain,token annotation time,annotation task,entity recognition,annotation data |
Field | DocType | Citations |
Data point,Active learning,Annotation,Computer science,Sampling (statistics),Natural language processing,Artificial intelligence,Named-entity recognition,Security token,Machine learning | Conference | 16 |
PageRank | References | Authors |
1.05 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ben Hachey | 1 | 321 | 24.83 |
| Beatrice Alex | 2 | 237 | 25.59 |
| Markus Becker | 3 | 16 | 1.05 |