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.
|Title of host publication||Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL-2005)|
|Publisher||Association for Computational Linguistics|
|Number of pages||8|
|Publication status||Published - 2005|