As the biomedical literature increases exponentially, biomedical named entity recognition (BNER) has become an important task in biomedical information extraction. In the previous studies based on deep learning, pretrained word embedding becomes an indispensable part of the neural network models, effectively improving their performance. However, the biomedical literature typically contains numerous polysemous and ambiguous words. Using fixed pretrained word representations is not appropriate. Therefore, this paper adopts the pretrained embeddings from language models (ELMo) to generate dynamic word embeddings according to context. In addition, in order to avoid the problem of insufficient training data in specific fields and introduce richer input representations, we propose a multitask learning multichannel bidirectional gated recurrent unit (BiGRU) model. Multiple feature representations (e.g., word-level, contextualized word-level, character-level) are, respectively, or collectively fed into the different channels. Manual participation and feature engineering can be avoided through automatic capturing features in BiGRU. In merge layer, multiple methods are designed to integrate the outputs of multichannel BiGRU. We combine BiGRU with the conditional random field (CRF) to address labels’ dependence in sequence labeling. Moreover, we introduce the auxiliary corpora with same entity types for the main corpora to be evaluated in multitask learning framework, then train our model on these separate corpora and share parameters with each other. Our model obtains promising results on the JNLPBA and NCBI-disease corpora, with F1-scores of 76.0% and 88.7%, respectively. The latter achieves the best performance among reported existing feature-based models.

中文翻译：

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基于多任务学习和上下文化词表示的多通道生物医学命名实体识别模型

随着生物医学文献的成倍增长，生物医学命名实体识别（BNER）已成为生物医学信息提取中的重要任务。在先前基于深度学习的研究中，预训练词嵌入已成为神经网络模型不可或缺的一部分，有效地提高了它们的性能。但是，生物医学文献通常包含许多多义和歧义的词。使用固定的预训练单词表示形式是不合适的。因此，本文采用来自语言模型（ELMo）的预训练嵌入来根据上下文生成动态词嵌入。另外，为了避免特定领域的训练数据不足的问题，并引入更丰富的输入表示，我们提出了一种多任务学习多通道双向门控循环单元（BiGRU）模型。多个特征表示（例如，单词级别，上下文化单词级别，字符级别）被分别或共同地馈送到不同的通道中。通过自动捕获BiGRU中的特征可以避免手动参与和特征工程。在合并层中，设计了多种方法来集成多通道BiGRU的输出。我们将BiGRU与条件随机字段（CRF）相结合，以解决序列标签中标签的依赖性。此外，我们为要在多任务学习框架中评估的主要语料引入具有相同实体类型的辅助语料库，然后在这些单独的语料上训练我们的模型并彼此共享参数。我们的模型在JNLPBA和NCBI疾病语料库上获得了可喜的结果，F1得分分别为76.0％和88.7％。在报告的现有基于特征的模型中，后者实现了最佳性能。