Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. They can also make the text less readable, especially in reference printed books, where they are extensively used. Abbreviations are especially problematic in low-resource settings, where systems are less robust to begin with. In this paper, we propose a new method for addressing the problems caused by a high density of domain-specific abbreviations in a text. We apply this method to the case of a Slovenian biographical lexicon and evaluate it on a newly developed gold-standard dataset of 51 Slovenian biographies. Our abbreviation identification method performs significantly better than commonly used ad-hoc solutions, especially at identifying unseen abbreviations. We also propose and present the results of a method for expanding the identified abbreviations in context.

改善和快速癌症诊断的关键组成部分是计算机辅助工具的发展。在本文中，我们提出了赢得SEGPC-2021竞争的解决方案，用于在显微镜图像中分割多发性骨髓瘤等离子体细胞。竞争数据集中使用的标签是生成半自动和呈现的噪声。要处理它，进行了沉重的图像增强程序，并使用自定义集合策略相结合了来自多种模型的预测。使用最先进的功能提取器和实例分段架构，导致SEGPC-2021最终测试集上的0.9389的平均交叉联盟。