Introduction
Text2ALM is an advanced information extraction tool turning implicit information in text into set of facts capturing key properties of entities mentioned in text. It relies on multiple linguistic and knowledge representation resources: Verbnet lexicon, Text2DRS tool, CoreNLP Stanford tool, LTH semantic role labeler, CoreALMLib knowledge library, CALM solver for knowledge representation language ALM.
Capabilities, Benefits, and Key Features
This is open-source software available via GitHub and has multiple other packages it inherently includes. Text2ALM is used for transforming text information into set of facts capturing key properties of entities mentioned in the ext.
Limitations / Technology Requirements
Text2ALM requires Python 3 on a Linux Operating System.
What can I expect?
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Support Tier: Tier 2 - Sponsor Support: If students were to contact IT Help, they refer the user to an individual sponsor, typically an instructor of a given course. The sponsor would work directly with the vendor on any unresolved issues.
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Acquisition Model: Direct Download: Software is downloaded to a local computer and installed to run from that specific device.
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Licensing Model: Local user license: The license is included with the initial installation. In rare cases, a local license file may need updated periodically to contiue to use the application. Check the entry for that specific application for instructions to do so.
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Authentication Model: None: No authentication is required after the application is acquired.
Who may use it?
How do I get started?
Clone the Git repository or download the .zip file via instructions in this link.
Is there a charge to me or my department?
The application is free and open-source.