Apache OpenNLP allows a developer to feed the system a block of text and using a collection of NLP (Neuro-Linguistic Programming) tools to detect various component parts.
OpenNLP will be able to detect sentences, sentence component parts, entity perform detection, text parsing & splitting, and many other complex operations.
The results of these tasks can be fed to other text processing engines that work with smaller text samples and wouldn't be able to handle larger blocks at the same efficiency.
OpenNLP can be used in search engines, document formatting, document parsing, Web crawlers, and so on.
Since the project uses machine learning methods, the more text you feed Apache OpenNLP, the better it gets at its job.
What is new in this release:
- Porter Stemmer tool
- L-BFGS parameter estimation
- Improved documentation
- Fine-grained POSTagger evaluation report
- Improved support to load user provided feature generator and context validation classes from OSGi environment
What is new in version 1.5.3:
- Porter Stemmer tool
- L-BFGS parameter estimation
- Improved documentation
- Fine-grained POSTagger evaluation report
- Improved support to load user provided feature generator and context validation classes from OSGi environment
What is new in version 1.5.2:
- Improved the white space handling in the Sentence Detector and its
- training code
- Added more cross validator command line tools
- Command line handling code has been refactored
- Fixed problems with the new build
- Now uses fast token class feature generation code by default
- Added support for BioNLP/NLPBA 2004 shared task data
- Removal of old and deprecated code
- Dictionary case sensitivity support is now done properly
- Support for OSGi
What is new in version 1.5.1:
- Wiki documentation converted to docbook
- F-Measure precision fix (OPENNLP-59)
- Perceptron bug fixes
- CoNLL 2003 training format support
- Chunker evaluation support
- Chunker supports now Portuguese Bosque AD format
- Chunker refactoring
Comments not found