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Description

If you are looking to tackle challenges related to text analysis or language processing, you've come to the perfect resource! GATE is a robust open-source software toolkit designed to address nearly any issue in text processing. It boasts a large and well-established community comprising developers, users, educators, students, and researchers. This toolkit is utilized by corporations, small to medium enterprises, research laboratories, and universities across the globe. The team behind GATE is composed of top-tier language processing developers. Being open-source, GATE is available at no cost, and users can seek free assistance from the community through GATE.ac.uk or opt for commercial support from our industrial partners. Remarkably, GATE stands out as the largest open-source language processing initiative, featuring a development team that is more than twice the size of its nearest competitors, many of which are integrated with GATE2. Over €5 million has been invested in the development of GATE, and our aim is to ensure that this investment continues to yield valuable returns for all users of the toolkit. By choosing GATE, you join a thriving ecosystem dedicated to advancing language processing technologies.

Description

At Iris.ai we have spent the last 6 years building an award-winning AI engine for scientific text understanding. Our algorithms for text similarity, tabular data extraction, domain-specific entity representation learning and entity disambiguation and linking measure up to the best in the world. On top of that, our machine builds a comprehensive knowledge graph containing all entities and their linkages to allow humans to learn from it, use it and also give feedback to the system. The Iris.ai Researcher Workspace is a flexible tool suite that allows to approach a project in a variety of ways. Modules include content based explorative search, machine analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents - and very powerful filters based on context descriptions, the machine’s analysis, or specific data points or entities. The Iris.ai engine for scientific text understanding is a powerful interdisciplinary system that can be automatically reinforced on a specific research field for much more nuanced machine understanding - without human training or annotation.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

GraphDB

Integrations

GraphDB

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

University of Sheffield

Founded

1995

Country

United Kingdom

Website

gate.ac.uk

Vendor Details

Company Name

Iris.ai

Founded

2015

Country

Norway

Website

iris.ai/

Product Features

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis

Product Features

Data Extraction

Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis

Alternatives

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