Search Results for "user"

...It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. Multi-user labeling sign up and login, when you create an annotation it's tied to your account. Configurable label formats let you customize the visual interface to meet your specific labeling needs. Support for multiple data types including images, audio, text, HTML, time-series, and video.

...While domain experts label their data, Label Sleuth automatically trains in the background-appropriate machine learning models. To avoid wasted labeling effort, Label Sleuth employs active learning techniques to guide the user in what they should be labeled next. Domain experts can quickly start labeling their data through an intuitive user interface. Developed by researchers across industry and academia, Label Sleuth incorporates the latest research from human-computer interaction, natural language processing, and artificial intelligence. Label Sleuth has been designed with an extensible architecture allowing the easy integration of new components, such as additional model architectures or active learning techniques.

...Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitate the creation and management of annotations. Active learning with uncertain sampling to query unlabeled data. Project tracking with real-time data aggregation and review process. User management panel with role-based access control.

This tool allows the user to annotate by coloring portions of the text. This can be seen as a simple model of annotation. This tool has been built to complete particular experimentation on student behavior (annotation per example) facing particularly difficult content. We put it here as an open-source project.

...Different types of tag markers can be incorporated e.g. grammatical, functional, semantic, linguistic markers.Tag-sets can be configured (modified/extended) by accessing the related table in the supporting database, The user can upload text files where sentences are normalized and inserted into the supporting database. Multiple narratives can be listed in the text file, where narratives are separated using a # symbol. The text upload process entitles the initial (POS) tagging of uploaded text using Stanford (POS) tagger. The user can later modify and extend the initial tagging. ...

...With a four step approach, DBpedia Spotlight performs named entity extraction, including entity detection and name resolution. It can also be used for named entity recognition, amongst other information extraction tasks. Empower the user experience reusing, interlinking and making semantic queries among high-quality open datasets, extracting meaning from unstructured data.

Quadriga is a web-application that acts as a clearing-house for text annotations -- in the form of contextualized triples, or “quadruples,” that form complex graphs -- generated with the Vogon desktop application (https://sourceforge.net/projects/gobtan/), and as an environment for managing text-annotation projects. It relies on a central authentication system for user authentication, a dictionary service (Wordpower) and an authority file service (Conceptpower). Quadriga can connect to a DSpace repository, allowing users to select items stored in the repository for annotation. Quadriga can use standard graphs to map quadruples onto conventional semantic graphs that can be submitted to a triple store, and used for interactive websites and visualizations.

A GUI-based text annotation tool for creating and visualizing annotations. It uses a flexible stand-off XML data format, and has advanced and customizable methods for information and relation visualization.

SemNotes is a semantic note taking tool for KDE4, built on top of Nepomuk-KDE. The tool is still under development, but it is already usable, provided that KDE4 is installed and the Nepomuk running.

Djangology is a web application for distributed collaborative text annotation. It consists of an admin interface for user/project/document management; an annotator interface, and an interface for error analysis and inter-annotator agreement statistics.

The Text Annotation Environment (tae) can be used to annotate natural language text manually or automatically (UIMA Annotator) with meta information (tokens, part-of-speech, named entities, ...). Tae is based on Eclipse and IBM's UIMA.