Communications of the ACM - Special issue on parallelism
Memory-Based Lexical Acquisition and Processing
Proceedings of the Third International EAMT Workshop on Machine Translation and the Lexicon
Simple features for Chinese word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
CHINERS: a Chinese named entity recognition system for the sports domain
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
| Hi-index | 0.01 |
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.