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
A trainable method for extracting Chinese entity names and their relations
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
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
Chinese named entity and relation identification system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
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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 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.