论文标题
人：分层通用模块化注释者
HUMAN: Hierarchical Universal Modular ANnotator
论文作者
论文摘要
许多现实现象很复杂，无法通过单个任务注释来捕获。这导致需要随后的注释，并具有相互依存的问题和答案，描述了主题的性质。即使在这种情况下，单个任务也很容易捕获现象，大多数注释工具的高专业化也可能导致如果任务仅稍微更改，则必须切换到另一个工具。 我们介绍了一种基于Web的新型注释工具人类，该工具通过a）涵盖文本和图像数据上的各种注释任务来解决上述问题，b）b）使用内部确定性状态机器，使研究人员能够以相互依存的方式链接不同的注释任务。此外，该工具的模块化性质使定义新的注释任务并集成了机器学习算法，例如用于主动学习。 Human具有易于使用的图形用户界面，可简化注释任务和管理。
A lot of real-world phenomena are complex and cannot be captured by single task annotations. This causes a need for subsequent annotations, with interdependent questions and answers describing the nature of the subject at hand. Even in the case a phenomenon is easily captured by a single task, the high specialisation of most annotation tools can result in having to switch to another tool if the task only slightly changes. We introduce HUMAN, a novel web-based annotation tool that addresses the above problems by a) covering a variety of annotation tasks on both textual and image data, and b) the usage of an internal deterministic state machine, allowing the researcher to chain different annotation tasks in an interdependent manner. Further, the modular nature of the tool makes it easy to define new annotation tasks and integrate machine learning algorithms e.g., for active learning. HUMAN comes with an easy-to-use graphical user interface that simplifies the annotation task and management.
