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IEPY is an open source tool for Information Extraction focused on Relation Extraction.
Web Scraping Tools
A corpus annotation tool with a web-based UI An active learning relation extraction tool pre-configured with convenient defaults. A rule based relation extraction tool for cases where the documents are semi-structured or high precision is required. A web-based user interface that: Allows layman users to control some aspects of IEPY and allows decentralization of human input. A shallow entity ontology with coreference resolution via Stanford CoreNLP An easily hack-able active learning core, ideal for scientist wanting to experiment with new algorithms.
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Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
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IEPY has a corpus annotation tool with a web-based UI, an active learning relation extraction tool pre-configured with convenient defaults and a rule based relation extraction tool for cases where the documents are semi-structured or high precision is required.
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IEPY is an open source tool for Information Extraction focused on Relation Extraction. IEPY has a corpus annotation tool with a web-based UI, an active learning relation extraction tool pre-configured with convenient defaults and a rule based relation extraction tool for cases where the documents are semi-structured or high precision is required.
To give an example of Relation Extraction, if the user is trying to find a birth date in: “John von Neumann (December 28, 1903 – February 8, 1957) was a Hungarian and American pure and applied mathematician, physicist, inventor and polymath.” Then IEPY’s task is to identify “John von Neumann” and “December 28, 1903” as the subject and object entities of the “was born in” relation. It’s aimed at users needing to perform Information Extraction on a large dataset and scientists wanting to experiment with new IE algorithms.
IEPY is great for technical documentation with information about parts and/or processes and financial reports about companies or products with georeferences, time references, references to other entities, or categorized in some way. The user can also sort through tons of user's comments and comments accumulated and organized by product or service or categorized by merchant or simply uncategorized.
IEPY works great with scientific content, academic literature, forums, sport/entertainment news, undisclosed documents, government documents, legal documents, wikis or any other unstructured text sources. IEPY supports both English and Spanish and will support other languages in the future.
To fully utilise EIPY you need humans in the loop to help IEPY train the models (Active Learning). Also to succeed with Information Extraction you should have one or more explicit relationships among entities that the user is pretty sure exist in the documents. IEPY is a very useful and effective extraction tool that will benefit any user.
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