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A novel machine learning approach for the identification of named entity relations

Tianfang Yao, Hans Uszkoreit
2005 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing - FeatureEng '05   unpublished

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In this paper, a novel machine learning approach for the identification of named entity relations (NERs) called positive and negative case-based learning (PNCBL) is proposed. It pursues the improvement of the identification performance for NERs through simultaneously learning two opposite cases and automatically selecting effective multi-level linguistic features for NERs and non-NERs. This approach has been applied to the identification of domain-specific and cross-sentence NERs for Chinese
more » ... ts. The experimental results have shown that the overall average recall, precision, and F-measure for 14 NERs are 78.50%, 63.92% and 70.46% respectively. In addition, the above F-measure has been enhanced from 63.61% to 70.46% due to adoption of both positive and negative cases.
doi:10.3115/1610230.1610232 fatcat:ugntyoxe65gtxh5paix5yfd4cu
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Tianfang Yao, Hans Uszkoreit. "A novel machine learning approach for the identification of named entity relations." Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing - FeatureEng '05 (2005)