DFKI-LT - A Novel Machine Learning Approach for the Identification of Named Entity Relations

Tianfang Yao, Hans Uszkoreit
A Novel Machine Learning Approach for the Identification of Named Entity Relations
1 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in NLP, Pages 1-8, Ann Arbor, ACL, 6/2005
 
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 texts. 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.
 
Files: BibTeX, W05-0401.pdf