Monday September 19, 2022

Hugging Face Transformers now enabled in Apache OpenNLP by ONNX Runtime

Apache OpenNLP 2.0 was released in early 2022 with a goal to start bridging the gap between modern deep learning NLP models and Apache OpenNLP’s ease of use as a Java NLP library. The addition of ONNX Runtime in Apache OpenNLP helps achieve that goal and does so without requiring any duplicate model training. ONNX Runtime is a runtime accelerator for models trained from all popular deep learning frameworks.[Read More]

Wednesday July 04, 2018

Announcing OpenNLP 1.9.0

The Apache OpenNLP team is pleased to announce the release of version 1.9.0 of Apache OpenNLP. The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, and parsing.

The OpenNLP 1.9.0 binary and source distributions are available for download from our download page:

The OpenNLP library is distributed by Maven Central as well. See the Maven Dependency page for more details:

Changes in this version:

  • Brat Document Parser should support name type filters
  • Brat format support fails on multi fragment annotations
  • Remove MD5 hashes from Release process
  • Use String[] instead of StringList in LanguageModel API
  • BRAT Annotator service Fails to start
  • Token model creation fails without at least one tag
  • Update Penn Treebank URL
  • Explain the new format of feature generator XML config
  • Unify code to sum up input context features
  • FeatureGeneratorUtil can recognize Japanese Hiragana and Katakana letters
For a complete list of fixed bugs and improvements please see the RELEASE_NOTES file included in the distribution.

The Apache OpenNLP Team

Wednesday December 27, 2017

Apache OpenNLP 2017 Year in Review


OpenNLP got off to a quick start in 2017 thanks to a 1.7.0 release on December 31, 2016. This version added support for Java 8 and set the tone for OpenNLP's 2017. In total, there were 7 releases in 2017. OpenNLP also got a new logo and website in 2017 with an updated look and easier navigation. OpenNLP also released its first model, a language detection model capable of identifying 103 languages. OpenNLP moved to GitHub for source management greatly simplifying the process of reviewing and merging pull requests. Some features and improvements that were added to OpenNLP in 2017 include:
  • A new language model CLI tool.
  • Moses format support.
  • CONLL-U format support.
  • Language codes now are ISO 639-3 compliant.
  • Many more unit tests.
  • Prefix and suffix feature generators are now configurable.
  • Learnable lemmatizer now returns all possible lemmas for a given word and part-of-speech tag.
  • A new language detection component and trained language model.
  • Evaluation tests now support ISO-639-3 language codes.
  • Fixed handling of xml parsers used through out the package.
  • New experimental API for word vectors and support for GloVe vector files.
  • Added annotator notes to BratAnnotator.
  • Add 20Newsgroups format support to the doccat component.
  • Resolved concurrency issue in POS tagger.

Community Development

Apache OpenNLP has added 6 new committers and PMC members in 2017.

Talks and Presentations

Apache OpenNLP was presented at several events in 2017 and there will be more OpenNLP talks in 2018 across the world.


OpenNLP had 7 releases in 2017. They were:

Release Timeline


The OpenNLP team was very excited to announce the language detection model's release on November 2, 2017. This model is capable of identifying 103 languages. The model is available for download from the OpenNLP website.


OpenNLP added 6 new committers and PMC members in 2017. There are currently 21 committers and 15 PMC members.


  • 289 JIRA tasks were closed in 2017.
  • 346 JIRA tasks were opened in 2017.


  • There were 269 closed pull requests.
  • There were 323 git commits throughout the year:

Notable Use of OpenNLP

OpenNLP powers an Air New Zealand Oscar chat bot.

“Air New Zealand uses OpenNLP to power its chatbot, Oscar. Launched in February 2017, Oscar provides a conversational interface for customers to ask questions about flights, amenities and policies. Using OpenNLP, we’ve been able to consistently provide over 50% conversational success and support hundreds of intents.”



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