An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition

时间 2021-01-09

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Abstract 基于传统的机器学习，其性能在很大程度上取决于特征工程。而且，这些方法是具有标记不一致问题的句子级方法。我们提出了一种神经网络方法，（Att-BiLSTM-CRF）用于文档NER。该方法利用通过Att获得的文档级全局信息来在文档中实施同一令牌的多个实例之间标记一致性 1 Introduction 在实践中，传统机器学习方法和深度学习方法都将NER视为句子级任务，即，它们将每

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1. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
2. Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
3. 《A Multi-task Approach for Named Entity Recognition in Social Media Data》论文笔记
4. Semi-supervised Bootstrapping approach for Named Entity Recognition论文笔记
5. 《Neural Architectures for Named Entity Recognition》
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7. END-TO-END NAMED ENTITY RECOGNITION AND RELATION EXTRACTION USING PRE-TRAINED LANGUAGE MODELS
8. Domain Adaptation for Object Recognition: An Unsupervised Approach
9. An Effective Approach to Unsupervised Machine Translation
10. [ICLR2018]Deep Active Learning for Named Entity Recognition
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1. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
2. Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
3. 《A Multi-task Approach for Named Entity Recognition in Social Media Data》论文笔记
4. Semi-supervised Bootstrapping approach for Named Entity Recognition论文笔记
5. 《Neural Architectures for Named Entity Recognition》
6. 论文阅读《Named Entity Recognition using an HMM-based Chunk Tagger》
7. END-TO-END NAMED ENTITY RECOGNITION AND RELATION EXTRACTION USING PRE-TRAINED LANGUAGE MODELS
8. Domain Adaptation for Object Recognition: An Unsupervised Approach
9. An Effective Approach to Unsupervised Machine Translation
10. [ICLR2018]Deep Active Learning for Named Entity Recognition

>>更多相关文章<<