On the Utility of a Syllable-Like Segmentation for Learning a Transliteration from English to an Indic Language (original) (raw)
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Assessing the challenge of fine-grained named entity recognition and classification
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Abstract 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.
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