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W05-0619.pdf
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🔗 Source: preview.aclanthology.org
📊 Size: 114 KB
👤 Author: Association for Computational Linguistics
⬇️ Downloads: 396

Summary

Researchers investigated the effects of selective sampling on annotation tasks in named entity recognition, specifically in the astronomy domain. They found that active learning, which involves selectively sampling more informative data points for human annotation, can reduce the amount of labeled data required to train a supervised learner. However, their results also showed that selective sampling can have an adverse effect on annotator accuracy and efficiency, with lower inter-annotator agreement and higher per-token annotation times. Despite this, the overall results confirmed the utility of active learning. The study used a committee-based method to estimate example informativity and employed a corpus of radio-astronomical abstracts, with four entity types: instrument-name, source-name, source-type, and spectral-feature. The researchers calculated inter-annotator agreement using accuracy and f-score metrics, with a pairwise f-score of 85.52 and accuracy of 97.15 for the Astronomy Bootstrapping Corpus.

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Description

Researchers investigated the effects of selective sampling on annotation tasks in named entity recognition, specifically in the astronomy domain.

Technical Information

  • File Format: PDF
  • File Size: 114 KB
  • Pages: 8
  • Language: EN
  • Author: Association for Computational Linguistics
  • Total Downloads: 396
  • Last Updated: 5 hours ago

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