论文标题
使用基于变压器的体系结构来解决投机和否定的范围
Resolving the Scope of Speculation and Negation using Transformer-Based Architectures
论文作者
论文摘要
猜测是文本数据中天然存在的现象，它构成了许多系统的组成部分，尤其是在生物医学信息检索域中。以前解决了提示检测和范围分辨率的工作（投机检测的两个子任务）范围从基于规则的系统到基于深度学习的方法。在本文中，我们将三个受欢迎的基于变压器的体系结构分别在这项任务上应用于两个公开可用的数据集，即Bioscope Corpus和SFU Review Corpus，报告了对先前报道的结果的实质性改进（在提示检测上至少0.29 f1点，在提示检测上至少有4.27 f1点）。我们还在多个数据集上尝试了模型的联合培训，这可以优于单个数据集训练方法。我们观察到XLNET始终优于Bert和Roberta，与其他基准数据集的结果相反。为了确认这一观察结果，我们将XLNET和Roberta应用于否定检测和范围解决方案，报告了最先进的生物镜语料库的否定范围分辨率（在Bioscope完整论文中增加了3.16 F1点，在生物学上的0.06 F1上的Bioscope摘要上的0.06 f1点）和SFU审查复习语料库（SFU Review coppus（增加0.3 f1 f1）。
Speculation is a naturally occurring phenomena in textual data, forming an integral component of many systems, especially in the biomedical information retrieval domain. Previous work addressing cue detection and scope resolution (the two subtasks of speculation detection) have ranged from rule-based systems to deep learning-based approaches. In this paper, we apply three popular transformer-based architectures, BERT, XLNet and RoBERTa to this task, on two publicly available datasets, BioScope Corpus and SFU Review Corpus, reporting substantial improvements over previously reported results (by at least 0.29 F1 points on cue detection and 4.27 F1 points on scope resolution). We also experiment with joint training of the model on multiple datasets, which outperforms the single dataset training approach by a good margin. We observe that XLNet consistently outperforms BERT and RoBERTa, contrary to results on other benchmark datasets. To confirm this observation, we apply XLNet and RoBERTa to negation detection and scope resolution, reporting state-of-the-art results on negation scope resolution for the BioScope Corpus (increase of 3.16 F1 points on the BioScope Full Papers, 0.06 F1 points on the BioScope Abstracts) and the SFU Review Corpus (increase of 0.3 F1 points).
