Processing 是一门开源编程语言和与之配套的集成开发环境(IDE)的名称。Processing 在电子艺术和视觉设计社区被用来教授编程基础,并运用于大量的新媒体和互动艺术作品中。

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论文题目: Learning Conceptual-Contextual Embeddings for Medical Text

论文摘要:

对于自然语言理解任务来说,外部知识通常是有用的。本文介绍了一个上下文文本表示模型,称为概念上下文(CC)嵌入,它将结构化的知识合并到文本表示中。与实体嵌入方法不同,文中提到的方法将知识图编码到上下文模型中。就像预先训练好的语言模型一样,CC嵌入可以很容易地在广泛的任务中重用。模型利用语义泛化,有效地编码了庞大的UMLS数据库。电子实验健康记录(EHRs)和医疗文本处理基准表明,而使得模型大大提高了监督医疗NLP任务的性能。

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Human beings learn and accumulate hierarchical knowledge over their lifetime. This knowledge is associated with previous concepts for consolidation and hierarchical construction. However, current incremental learning methods lack the ability to build a concept hierarchy by associating new concepts to old ones. A more realistic setting tackling this problem is referred to as Incremental Implicitly-Refined Classification (IIRC), which simulates the recognition process from coarse-grained categories to fine-grained categories. To overcome forgetting in this benchmark, we propose Hierarchy-Consistency Verification (HCV) as an enhancement to existing continual learning methods. Our method incrementally discovers the hierarchical relations between classes. We then show how this knowledge can be exploited during both training and inference. Experiments on three setups of varying difficulty demonstrate that our HCV module improves performance of existing continual learning methods under this IIRC setting by a large margin. Code is available in https://github.com/wangkai930418/HCV_IIRC.

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