Fault-Tolerant Learning for Term Extraction

This paper presents the Fault-Tolerant Learning approach for term extraction. The approach extracts terms using automatically generated seeds instead of prior domain knowledge or annotated corpora. Thus it is applicable to any domain specific corpus and it is especially useful for resource-limited domains. Two classifiers are separately trained for prediction and verification to ensure the performance of the proposed approach. Evaluations conducted on two different domains for Chinese term extraction show significant improvements over existing techniques and also verify the efficiency and relative domain independent nature of the approach.