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We introduce the brat rapid annotation tool (BRAT), an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annotation for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. We discuss several case studies of real-world annotation projects using pre-release versions of BRAT and present an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task, showing a 15% decrease in total annotation time. . | BRAT a Web-based Tool for NLP-Assisted Text Annotation Pontus Stenetorp1 Sampo Pyysalo2 3 Goran Topic1 Tomoko Ohta1 2 3 Sophia Ananiadou2 3 and Jun ichi Tsujii4 1 Department of Computer Science The University of Tokyo Tokyo Japan 2School of Computer Science University of Manchester Manchester UK 3National Centre for Text Mining University of Manchester Manchester UK 4Microsoft Research Asia Beijing People s Republic of China pontus smp goran okap @is.s.u-tokyo.ac.jp sophia.ananiadou@manchester.ac.uk jtsujii@microsoft.com Abstract We introduce the brat rapid annotation tool BRAT an intuitive web-based tool for text annotation supported by Natural Language Processing NLP technology. BRAT has been developed for rich structured annotation for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. We discuss several case studies of real-world annotation projects using pre-release versions of BRAT and present an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task showing a 15 decrease in total annotation time. BRAT is available under an opensource license from http brat.nlplab.org 1 Introduction Manually-curated gold standard annotations are a prerequisite for the evaluation and training of state-of-the-art tools for most Natural Language Processing NLP tasks. However annotation is also one of the most time-consuming and financially costly components of many NLP research efforts and can place heavy demands on human annotators for maintaining annotation quality and consistency. Yet modern annotation tools are generally technically oriented and many offer little support to users beyond the minimum required functionality. We believe that intuitive and userfriendly interfaces as well as the judicious application of NLP technology to support not supplant human judgements can help maintain the quality of annotations make annotation more .
