摘要：Previous rule-based approaches for Named Entity Recognition (NER) in German base NER on Part-of-Speech tagged texts. We present a new approach where NER is situated between morphological analysis and Part-of-Speech Tagging and model the NER-grammar entirely with weighted finite state transducers (WFST). We show that NER strategies like the resolution of proper noun/common noun or company-name/family-name ambiguities can be formulated as a best path function of a WFST. The frequently used second pass resolution of coreferential Named Entities can be formulated as a re-assignment of appropriate weights. A prototypical NE recognition system built on the basis of WSFT and large lexical resources was tested on a manually annotated corpus of 65,000 tokens. The results show that our system compares in recall and precision to existing rule-based approaches.
关键词：Named Entity Recognition ; weighted finite state transducers ; large lexical resources
