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What is I.R.I.S. CRISALIS?

Similar to how a caterpillar transforms into a butterfly, the CRISALIS® software will transform your unwieldy information system into a sleek and effective tool. This "super-app" unites premier natural resource applications, relevant GIS/mapping datasets, and a vast library of technical documents into a unified Natural Resource Information System. Since its launch in 1999, our award-winning team has carefully designed, developed, and released CRISALIS®, with the latest version reflecting over seven years of focused refinement. It establishes the foundation and provides the crucial framework for an All-In-One Natural Resource Information System that is suitable for organizations of any size, particularly smaller to mid-sized ones with limited IT capabilities. By linking more than 200 applications related to Forestry and Natural Resources, including 50 commercial choices, CRISALIS® can be seamlessly tailored to incorporate the applications, datasets, and documents you already use. Moreover, it enables you to quickly and efficiently access and manage over 200,000 layers of Natural Resources data, making it an essential tool for effective information management. With this extraordinary software, not only is productivity enhanced, but users can also easily explore and utilize a vast reservoir of information right at their fingertips. Ultimately, CRISALIS® stands out as a pivotal resource for organizations seeking to maximize their operational efficiency and access to critical data.

What is LexVec?

LexVec is an advanced word embedding method that stands out in a variety of natural language processing tasks by factorizing the Positive Pointwise Mutual Information (PPMI) matrix using stochastic gradient descent. This approach places a stronger emphasis on penalizing errors that involve frequent co-occurrences while also taking into account negative co-occurrences. Pre-trained vectors are readily available, which include an extensive common crawl dataset comprising 58 billion tokens and 2 million words represented across 300 dimensions, along with a dataset from English Wikipedia 2015 and NewsCrawl that features 7 billion tokens and 368,999 words in the same dimensionality. Evaluations have shown that LexVec performs on par with or even exceeds the capabilities of other models like word2vec, especially in tasks related to word similarity and analogy testing. The implementation of this project is open-source and is distributed under the MIT License, making it accessible on GitHub and promoting greater collaboration and usage within the research community. The substantial availability of these resources plays a crucial role in propelling advancements in the field of natural language processing, thereby encouraging innovation and exploration among researchers. Moreover, the community-driven approach fosters dialogue and collaboration that can lead to even more breakthroughs in language technology.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

$2900 one-time payment

Pricing Information

Free
Free Version

Supported Platforms

SaaS
Android
Windows

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

I.R.I.S. Institute

Date Founded

1995

Company Location

United States

Company Website

www.irisinfosys.org/crisalis.html

Company Facts

Organization Name

Alexandre Salle

Company Location

Brazil

Company Website

github.com/alexandres/lexvec

Categories and Features

Forestry

Barcoding / RFID
Contract Management
Cost Accounting
Customer Management
Financial Management
Harvest Management
Order Processing
Pricing Management
Production Tracking
Supplier Management
Transportation Management

Categories and Features

Embedding Models

Not specified

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