Identifying Semantic Relations Between Named Entities from Chinese Texts

COLLATE is a project dedicated to building up a German authority center for language technology in Saarbrücken. Under this project, a computational model with three-stage pipeline architecture for Chinese information extraction has been proposed. In this paper, we concentrate on the presentation for the third stage, viz., the identification of named entity relations (NERs). A learning and identification approach for NERs called positive and negative case-based learning and identification is described in detail. It pursues the improvement of the identification performance for NERs through simultaneously learning two opposite cases, automatically selecting effective multi-level linguistic features for each NER and non-NER, and optimally achieving an identification tradeoff etc. 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.