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cs.CL2020★ 2 cited
Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition
Arda Akdemir, Tetsuo Shibuya
Developing high-performing systems for detecting biomedical named entities has major implications. State-of-the-art deep-learning based solutions for entity recognition often requi…
cs.CL2020★ 1 cited
Hierarchical Multi Task Learning with Subword Contextual Embeddings for Languages with Rich Morphology
Arda Akdemir, Tetsuo Shibuya, Tunga Güngör
Morphological information is important for many sequence labeling tasks in Natural Language Processing (NLP). Yet, existing approaches rely heavily on manual annotations or externa…