Inducing Gazetteer for Chinese Named Entity Recognition Based on Local High-Frequent Strings

Gazetteers, or entity dictionaries, are important for named entity recognition (NER). Although the dictionaries extracted automatically by the previous methods from a corpus, web or Wikipedia are very huge, they also misses some entities, especially the domain-specific entities. We present a novel method of automatic entity dictionary induction, which is able to construct a dictionary more specific to the processing text at a much lower computational cost than the previous methods. It extracts the local high-frequent strings in a document as candidate entities, and filters the invalid candidates with the accessor variety (AV) as our entity criterion. The experiments show that the obtained dictionary can effectively improve the performance of a high-precision baseline of NER.