Analysing wikipedia and gold-standard corpora for NER training

Abstract

Named entity recognition (NER) for English typically involves one of three gold standards: MUC, CoNLL, or BBN, all created by costly manual annotation. Recent work has used Wikipedia to automatically create a massive corpus of named entity annotated text. We present the first comprehensive cross-corpus evaluation of NER. We identify the causes of poor cross-corpus performance and demonstrate ways of making them more compatible. Using our process, we develop a Wikipedia corpus which outperforms gold standard corpora on cross-corpus evaluation by up to 11%. © 2009 Association for Computational Linguistics.