Analysing Wikipedia and Gold-Standard Corpora for NER Training

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 crosscorpus 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 crosscorpus evaluation by up to 11%.

• Type: Conference
• Year: 2009
• Where: EACL
• Authors: Joel Nothman, Tara Murphy, James R. Curran
• Keywords: Costly Manual Annotation, EACL 2009, Entity Annotated Text, Gold Standard Corpora, Natural Language Processing