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TWNERTC and EWNERTC are collections of automatically categorized and annotated sentences obtained from Turkish and English Wikipedia for named-entity recognition and text categorization. Firstly, we construct large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic knowledge base, Freebase. The final gazetteers has 77 domains (categories) and more than 1000 fine-grained entity types for both languages. Turkish gazetteers contains approximately 300K named-entities and English gazetteers has approximately 23M named-entities. By leveraging large-scale gazetteers and linked Wikipedia articles, we construct TWNERTC and EWNERTC. Since the categorization and annotation processes are automated, the raw collections are prone to ambiguity. Hence, we introduce two noise reduction methodologies: (a) domain-dependent (b) domain-independent. We produce two different versions by post-processing raw collections. As a result of this process, we introduced 3 versions of TWNERTC and EWNERTC: (a) raw (b) domain-dependent post-processed (c) domain-independent post-processed. Turkish collections have approximately 700K sentences for each version (varies between versions), while English collections contain more than 7M sentences. We also introduce "Coarse-Grained NER" versions of the same datasets. We reduce fine-grained types into "organization", "person", "location" and "misc" by mapping each fine-grained type to the most similar coarse-grained version. Note that this process also eliminated many domains and fine-grained annotations due to lack of information for coarse-grained NER. Hence, "Coarse-Grained NER" labelled datasets contain only 25 domains and number of sentences are decreased compared to "Fine-Grained NER" versions. All processes are explained in our published white paper for Turkish; however, major methods (gazetteers creation, automatic categorization/annotation, noise reduction) do not change for English.
H. Bahadir Sahin (Thu,) studied this question.