AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging
复制标题
DOI：
10.18653/v1/p19-3010
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Bill Y. Lin;Dong-Ho Lee;Frank F. Xu;Ouyu Lan;Xiang Ren
中科院分区：
文献类型：
--
作者：
Bill Y. Lin;Dong-Ho Lee;Frank F. Xu;Ouyu Lan;Xiang Ren
我们介绍了一个开源的基于Web的数据标注框架(AlpacaTag)，用于命名实体识别(NER)等序列标注任务。羊驼标签的独特优势有三个方面。1)主动智能推荐：动态建议注释，并使用后端主动学习模型对最具信息量的未标记实例进行采样；2)自动人群整合：通过合并来自多个注释器的不一致标签，增强注释者之间的实时一致性；3)实时模型部署：用户可以在进行新注释的同时在下游系统中部署自己的模型。AlpacaTag是一款针对序列标签任务的全面解决方案，范围从基于主动学习的建议的快速标签和人群注释的自动合并到实时模型部署。
We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER). The distinctive advantages of AlpacaTag are three-fold. 1) Active intelligent recommendation: dynamically suggesting annotations and sampling the most informative unlabeled instances with a back-end active learned model; 2) Automatic crowd consolidation: enhancing real-time inter-annotator agreement by merging inconsistent labels from multiple annotators; 3) Real-time model deployment: users can deploy their models in downstream systems while new annotations are being made. AlpacaTag is a comprehensive solution for sequence labeling tasks, ranging from rapid tagging with recommendations powered by active learning and auto-consolidation of crowd annotations to real-time model deployment.