Manual annotation of textual documents is a necessary task when constructing benchmark corpora for training and evaluating machine learning algorithms. We created a comprehensive directory of annotation tools that currently includes 93 tools. We analyzed the tools over a set of 31 features and implemented simple scripts and a Web application that filters the tools based on chosen criteria. We present two use cases using the directory and propose ideas for its maintenance. The directory, source codes for scripts, and link to the Web application are available at: https://github.com/mariananeves/annotation-tools
翻译:在建立用于培训和评估机器学习算法的基准公司时,手册文档的手工说明是一项必要任务。我们创建了一个说明工具综合目录,目前包括93个工具。我们分析了31个功能组的工具,并应用了简单的脚本和一个网络应用程序,根据选定的标准过滤工具。我们用该目录介绍两个使用案例,并提出维护该目录的想法。目录、脚本源代码和与网络应用程序的链接见:https://github.com/mariananeves/annovenation-tools。