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Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER

2022-04-05 · SemEval (NAACL) 2022 7 · Amit Pandey, Swayatta Daw, Vikram Pudi

We investigate the task of complex NER for the English language. The task is non-trivial due to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities in the prevalent literature. Using pre-trained language models such as BERT, we obtain a competitive performance on this task. We qualitatively analyze the performance of multiple architectures for this task. All our models are able to outperform the baseline by a significant margin. Our best performing model beats the baseline F1-score by over 9%.

📄 PDF Abstract BibTeX arXiv:2204.02173

Code (1)

amitpandey-research/complex_ner 공식 구현 pytorch

Tasks

NER

Methods 이 논문이 사용한 방법론

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Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…
Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…
Multi-Head Attention 설명 없음
Residual Connection 설명 없음
WordPiece 설명 없음

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