Analysis and repair of name tagger errors

Abstract

Name tagging is a critical early stage in many natural language processing pipelines. In this paper we analyze the types of errors produced by a tagger, distinguishing name classification and various types of name identification errors. We present a joint inference model to improve Chinese name tagging by incorporating feedback from subsequent stages in an information extraction pipeline: name structure parsing, cross-document coreference, semantic relation extraction and event extraction. We show through examples and performance measurement how different stages can correct different types of errors. The resulting accuracy approaches that of individual human annotators.