Assessing the Challenge of Fine-Grained Named Entity Recognition and Classification
نویسندگان
چکیده
Named Entity Recognition and Classification (NERC) is a well-studied NLP task typically focused on coarse-grained named entity (NE) classes. NERC for more fine-grained semantic NE classes has not been systematically studied. This paper quantifies the difficulty of fine-grained NERC (FG-NERC) when performed at large scale on the people domain. We apply unsupervised acquisition methods to construct a gold standard dataset for FG-NERC. This dataset is used to benchmark methods for classifying NEs at various levels of fine-grainedness using classical NERC techniques and global contextual information inspired fromWord Sense Disambiguation approaches. Our results indicate high difficulty of the task and provide a ‘strong’ baseline for future research.
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