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Multilayer ToI Detection Approach for Nested NER
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DOI：10.1109/ACCESS.2019.2961118.png)
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摘要
En 中文
Nested entities commonly exist in news articles and biomedical corpora. The performance of nested NER is still a great challenge in the field of named entity recognition (NER). Unlike the structural models in previous work, this paper presents a comprehensive study of nested NER by means of text-of-interest (ToI) detection. This paper presents a novel ToI-CNN with dual transformer encoders (ToI-CNN DTE) model for this solution. We design a directional self-attention mechanism to encode contextual representation over the whole-sentence in the forward and backward directions. The features of the entities are extracted from the contextual token representations by a convolutional neural network. Moreover, we use HAT pooling operation to convert the various length ToIs to a fixed length vector and connect with a fully connected network for classification. The layer where the nested entities are located can be evaluated by multi-task learning jointly with layer classification. The experimental results show that our model achieves excellent performance in F1 score, training cost and layer evaluation on the nested NER datasets.
Keyword:
Nested NER
information extraction
transformer encoder
convolutional neural network
pooling operation
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3.6
论文数:
9.8W
被引数:
29.4W
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