论文标题
NLNDE：增强具有注意力的神经序列标记和嘈杂的药理实体检测
NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection
论文作者
论文摘要
命名实体识别已在英语新闻文本上进行了广泛的研究。但是，转移到其他领域和语言仍然是一个具有挑战性的问题。在本文中，我们描述了我们参与Bionlp开放共享任务的Pharmaconer竞争的第一个副本的系统。针对西班牙文本中的药理学实体检测，该任务提供了非标准的域和语言设置。但是，我们提出了一个既不需要语言也不需要域专业知识的体系结构。我们将任务视为序列标记任务，并通过基于注意力的嵌入选择和自动注释数据的培训进行实验，以进一步提高系统的性能。我们的系统取得了令人鼓舞的结果，尤其是通过组合不同的技术，并且在竞争中达到了88.6％的F1。
Named entity recognition has been extensively studied on English news texts. However, the transfer to other domains and languages is still a challenging problem. In this paper, we describe the system with which we participated in the first subtrack of the PharmaCoNER competition of the BioNLP Open Shared Tasks 2019. Aiming at pharmacological entity detection in Spanish texts, the task provides a non-standard domain and language setting. However, we propose an architecture that requires neither language nor domain expertise. We treat the task as a sequence labeling task and experiment with attention-based embedding selection and the training on automatically annotated data to further improve our system's performance. Our system achieves promising results, especially by combining the different techniques, and reaches up to 88.6% F1 in the competition.
