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Accelerating the annotation of sparse named entities by dynamic sentence selection
Yoshimasa Tsuruoka1, Jun'ichi Tsujii, Sophia Ananiadou
1School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester M17DN, UK. yoshimasa.tsuruoka@manchester.ac.uk
BMC Bioinformatics
|December 17, 2008
Summary
This study introduces a novel framework to reduce human effort in creating named entity annotations for corpora. The approach minimizes manual review, especially for sparse named entities, improving efficiency in information extraction.
Area of Science:
- Natural Language Processing
- Machine Learning
- Information Extraction
Background:
- Machine learning models like CRFs and SVMs achieve good accuracy in named entity recognition.
- Lack of annotated corpora hinders practical application of ML-based named entity recognition.
Purpose of the Study:
- To present an active learning-like framework to reduce human effort in creating named entity annotations.
- To enable practical information extraction systems by addressing data scarcity.
Main Methods:
- An iterative and interactive annotation process between human annotators and a probabilistic named entity tagger.
- A framework designed to annotate all occurrences of target named entities, avoiding sampling bias.
Main Results:
- The proposed framework significantly reduces the number of sentences requiring human examination for annotation.
- Cost reduction is particularly drastic when target named entities are sparse.
Conclusions:
- The framework effectively minimizes human effort in corpus annotation for named entity recognition.
- This approach facilitates the development of practical information extraction systems by overcoming data annotation challenges.
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