CoNLL(Conference on Computational Natural Language Learning)是一个顶级会议,每年由SIGNLL(ACL的自然语言学习特别兴趣小组)组织。官网链接:https://www.conll.org/

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Linear chain conditional random fields (CRFs) combined with contextual word embeddings have achieved state of the art performance on sequence labeling tasks. In many of these tasks, the identity of the neighboring words is often the most useful contextual information when predicting the label of a given word. However, contextual embeddings are usually trained in a task-agnostic manner. This means that although they may encode information about the neighboring words, it is not guaranteed. It can therefore be beneficial to design the sequence labeling architecture to directly extract this information from the embeddings. We propose locally-contextual nonlinear CRFs for sequence labeling. Our approach directly incorporates information from the neighboring embeddings when predicting the label for a given word, and parametrizes the potential functions using deep neural networks. Our model serves as a drop-in replacement for the linear chain CRF, consistently outperforming it in our ablation study. On a variety of tasks, our results are competitive with those of the best published methods. In particular, we outperform the previous state of the art on chunking on CoNLL 2000 and named entity recognition on OntoNotes 5.0 English.

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Linear chain conditional random fields (CRFs) combined with contextual word embeddings have achieved state of the art performance on sequence labeling tasks. In many of these tasks, the identity of the neighboring words is often the most useful contextual information when predicting the label of a given word. However, contextual embeddings are usually trained in a task-agnostic manner. This means that although they may encode information about the neighboring words, it is not guaranteed. It can therefore be beneficial to design the sequence labeling architecture to directly extract this information from the embeddings. We propose locally-contextual nonlinear CRFs for sequence labeling. Our approach directly incorporates information from the neighboring embeddings when predicting the label for a given word, and parametrizes the potential functions using deep neural networks. Our model serves as a drop-in replacement for the linear chain CRF, consistently outperforming it in our ablation study. On a variety of tasks, our results are competitive with those of the best published methods. In particular, we outperform the previous state of the art on chunking on CoNLL 2000 and named entity recognition on OntoNotes 5.0 English.

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