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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Language models (LMs) pretrained on a large text corpus and fine-tuned on a downstream text corpus and fine-tuned on a downstream task becomes a de facto training strategy for several natural language processing (NLP) tasks. Recently, an adaptive pretraining method retraining the pretrained language model with task-relevant data has shown significant performance improvements. However, current adaptive pretraining methods suffer from underfitting on the task distribution owing to a relatively small amount of data to re-pretrain the LM. To completely use the concept of adaptive pretraining, we propose a back-translated task-adaptive pretraining (BT-TAPT) method that increases the amount of task-specific data for LM re-pretraining by augmenting the task data using back-translation to generalize the LM to the target task domain. The experimental results show that the proposed BT-TAPT yields improved classification accuracy on both low- and high-resource data and better robustness to noise than the conventional adaptive pretraining method.
