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We describe a simple XML format to share text documents and annotation
A minimalist approach to share text documents and data annotations. Allows a large number of different annotations to be represented.
Project files contain:
- simple code to hold/read/write data and perform sample processing.
- BioC-formatted corpora
- BioC tools that work with BioC corpora
BioC goals
- simplicity
- interoperability
- broad use
- reuse
There should be little investment required to learn to use a format or a software module to process that format. We are...
MARF is a general cross-platform framework with a collection of algorithms for audio (voice, speech, and sound) and natural language text analysis and recognition along with sample applications (identification, NLP, etc.) of its use, implemented in Java.
This project aims to build a suite of Natural Language Processing tools. Modules will include corpus indexing and access tools, a part-of-speech tagger, tokenisers, text classification software, etc.
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This is a Java-based project for complex event extraction from text and co-reference resolution. Currently the code can read BioNLP shared task format (http://2011.bionlp-st.org/) and i2b2 Natural Language Processing for Clinical Data shared task format (https://www.i2b2.org/NLP/DataSets/Main.php). Event extraction includes finding events and the parameters for an event in a text.
Arabisc is speaker independent large vocabulary continuous speech recognizer for Arabic language released under GNU license.It is also a collection of open source tools that allows researchers and developers to build speech recognition systems for Arab
D.U.C.K (Determine segmentation of Unknown words by using Context Knowledge)is an NLP tool, which aims to find the correct segmentation for unknown words in written Hebrew. Statistics from different scopes will be used to determine the segmentation.
MutationFinder is a biomedical natural language processing (NLP) system for extracting mentions of point mutations from free text. MutationFinder achieves high performance (99% precision, 81% recall on blind test data) as an information extraction system
Leo is a project to provide an architecture for defining XML
specifications of grammars for different natural language parsing
systems and tools for using that architecture to permit sharing of
grammar resources across different systems.
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