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• W2883818012 abstract "This study improves the performance of neural named entity recognition by a margin of up to 11% in terms of F-score on the example of a low-resource language like German, thereby outperforming existing baselines and establishing a new state-of-the-art on each single open-source dataset (CoNLL 2003, GermEval 2014 and Tubingen Treebank 2018). Rather than designing deeper and wider hybrid neural architectures, we gather all available resources and perform a detailed optimization and grammar-dependent morphological processing consisting of lemmatization and part-of-speech tagging prior to exposing the raw data to any training process. We test our approach in a threefold monolingual experimental setup of a) single, b) joint, and c) optimized training and shed light on the dependency of downstream-tasks on the size of corpora used to compute word embeddings." @default.
• W2883818012 created "2018-08-03" @default.
• W2883818012 creator A5003022499 @default.
• W2883818012 creator A5008340710 @default.
• W2883818012 date "2018-12-01" @default.
• W2883818012 modified "2023-09-25" @default.
• W2883818012 title "Resource-Size Matters: Improving Neural Named Entity Recognition with Optimized Large Corpora" @default.
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• W2883818012 doi "https://doi.org/10.1109/icmla.2018.00149" @default.
• W2883818012 hasPublicationYear "2018" @default.
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