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Speaker: Artem Chernodub, Chief Scientist at Clikque Technology and Associate Professor at Ukrainian Catholic University
Summary: Sequence Tagging is an important NLP problem that has several applications, including Named Entity Recognition, Part-of-Speech Tagging, and Argument Component Detection. In our talk, we will focus on a BiLSTM+CNN+CRF model — one of the most popular and efficient neural network-based models for tagging. We will discuss task decomposition for this model, explore the internal design of its components, and provide the ablation study for them on the well-known NER 2003 shared task dataset.