Ensemble Approach to Extract Chemical Named Entity by Using Results of Multiple CNER Systems with Different Characteristic
نویسندگان
چکیده
We propose a novel ensemble approach chemical named entity recognition (CNER) tool that uses different CNER tools such as OSCAR4 and ChemSpot with different characteristics by using machine learning (ML) technique. Since this tool may identify typical errors of one CNER by using other tools’ output, our system outperforms ChemSpot (ML-based) and OSCAR4 (rule-based) in original setting.
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