The best free text labeling tools for text annotation and categorization in Natural Language Processing
Brat, INCEpTION and DACCANO have been described as the three best free text annotation tools that are suitable for manually labeling documents in Natural Learning Process (NLP) projects. This article will at length describe what this tool is suitable for, and then describe the installation and configuration process and its usage.
Text Annotation is the act of practicing and the result of adding a note to a text which may include comments, footnotes, highlights, and links. Text annotation can either be for private or shared reading. Its purpose is either collaborative writing and editing, commentary, reading or sharing. Text annotations help to train Natural Learning Process algorithms which require large annotate text datasets.
It is also known as text classification. Text classification ensures that annotators read a text or a group of texts. Text classification annotates an entire body or line of text with a single label.
BRAT is an online or virtual environment used for a combined text annotation that can be simultaneously run on a server and then used in a browser. It is used to annotate single expressions and the relationships in between. Hence, using BRAT to annotate longer text such as paragraphs is not convenient. Input documents must come as text files. It is often argued that the user interface (UI) presentation of the text file in brat is not like its original formatting. Due to these reasons, BRAT is not noted to be an ideal tool for annotating structured documents if you would prefer to directly annotate PDFs. The annotations are also stored in text files. BRAT has some major functionalities for collaborative labeling which are: multiple users are supported and there is an integrated annotation comparison.
DOCCANO is another annotation tool which is mainly for text files. It is believed to be easier and simpler to use than brat. Like BRAT, it runs server-based and has a browser user interface. However, it differs from BRAT in the sense that configuration of any kind is done in the web user interface. Its use case is limited to document classification, sequence labeling and sequence-to-sequence. Most importantly, DACANO is user-friendly as compared to BRAT and depending on the choice of the use case, labels are only on document level or span level. Based on the project type, you can determine the options for the annotation export format, which can either be CSV or JSON-based. Again, DOCCANO allows multiple users. However, unlike BRAT, DACCANO does not have additional features for collaborative labeling. Again, DOCCANO provides two extra features that are not available in BRAT. These features include writing and saving labeling guidelines right within the app (in Markdown) and getting a basic diagrammatic overview of the labeling stats.
INCEpTION is the follow-up project to WebAnno. Similarly, INCEpTION uses a browser user interface. It can be used in diverse ways either as a group of users on a server or as a standalone version. INCEpTION is noted to be a heavier tool than either DOCCANO or BRAT. It can be used for either text files or PDFs that contain text information. INCEpTION has an extensive feature that enables you to configure virtually everything. Again, it eases collaborative labeling and can statistically evaluate the annotations while exporting annotations in a broad range of common Natural Learning Process labeling formats. Nonetheless, INCEpTION can be complicated to use initially, and it has been advised to ignore the features that are complex to use. Due to its PDF labeling capacity, most people are drawn to using INCEpTION.
BRAT comes with detailed instructions on how to install it. If you just want to install and run brat on your local machine, then the standalone server is what you want. Firstly, you must place the data section of the instructions to learn how to set up the annotation files. As BRAT is not compatible with Python, you would have to modify the command python standalone.py to python2 standalone.py. BRAT is noted to work exceptionally well with Google Chrome.
DOCCANO is easier to use. When installing DOCCANO, you don’t necessarily need to understand what Docker is. This can be done provided Docker is installed. To get abreast with its functionality, try out doccano's live demos.
INCEpTION provides a comprehensive user guide that describes at length how to install and run it. Running INCEpTION is especially easy, because you can execute the downloaded JAR file without installing it.
BRAT allows configuring a project-specific labeling scheme through .conf files. Using brat is fairly straightforward. Firstly, you must mark a text span which opens a pop-up menu. The options in the menu may depend on the configuration of the labeling scheme. However, it is necessarily easy to mark the exact desired span. Furthermore, if the marked span is too long, the pop-up menu may not fit on the screen.
INCEpTION demands a lot of configurations. INCEpTION has a lot of PDF labeling capacity. In creating a new project, you firstly must create a new project after which you must import a document. Then, you must define a label, change the document viewer settings to display the document as a PDF file and then annotate the document.
Unlike INCEpTION, DOCCANO does not demand too much configuration. DOCCANO allows you to create and edit labels directly in the browser user interface as well as labeling guidelines. To get abreast with DOCCANO’s functionality, it is recommended to try out DOCCANO’s live demos.
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This is a text annotation tool that can be used to annotate text automatically or manually. Tagtog supports PDF annotation and includes pre-trained NER models for automatic text annotation.
Scale provides text annotation services such as text categorization, comparison, and OCR transcription. Scale provides computer vision and NLP data annotation services.
The LightTag text annotation tool is a platform for annotators and companies to label their text data in house.
This is a text annotation tool that helps to efficiently classify and annotate medical data. KConnect provides semantic annotation, text analysis, and semantic search services for medical information.
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