Awesome
<!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->Welcome to Apache OpenNLP!
The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text.
This sandbox of the toolkit is written mostly in Java and provides support for special NLP tasks, such as word sense disambiguation, coreference resolution, text summarization, and more! These tasks are usually required to build text processing services.
The goal of the OpenNLP sandbox is to provide extra components, potentially in an experimental stage.
OpenNLP sandbox code can be used both programmatically through its Java API, some components even from a terminal through its CLI.
Useful Links
For additional information, visit the OpenNLP Home Page
You can use OpenNLP with any language, demo models are provided here. The models are fully compatible with the latest release, they can be used for testing or getting started.
[!NOTE]
Please train your own models for all other use cases.
Documentation, including JavaDocs, code usage and command-line interface examples are available here
You can also follow our mailing lists for news and updates.
Overview
Currently, the library has different packages:
caseeditor-corpus-server-plugin
: A set of Java classes for Apache UIMA as Eclipse plugin to integrate corpora.caseeditor-opennlp-plugin
: An OpenNLP plugin for Apache UIMA.corpus-server
: A multi-module component to create, search, remove, and serve multiple corpora.mahout-addon
: An addon for Apache Mahout.mallet-addon
: An addon for Mallet targeting topic modelling techniques.opennlp-coref
: A component to conduct co-reference resolution.modelbuilder-addon
: A set of classes to build models.nlp-utils
: A set of OpenNLP util classes.opennlp-dl
: An adapter component for deeplearning4j.opennlp-similarity
: A set of components that solve a number of text processing and search tasks, see further details in this README.md.opennlp-wsd
: A set of components that allow for word sense disambiguation.summarizer
: A set of classes providing text summarization.tagging-server
: A RESTful webservice to allow for NER, POS tagging, sentence detection and tokenization.tf-ner-poc
: An adapter component for Tensorflow, in an early proof-of-concept (poc) stage.wikinews-importer
: A set of classes to process and annotate text formatted in MediaWiki markup.
Getting Started
You can import the core toolkit directly from Maven, SBT or Gradle after you have build it locally:
Maven
<dependency>
<groupId>org.apache.opennlp</groupId>
<artifactId>opennlp-sandbox</artifactId>
<version>${opennlp.version}</version>
</dependency>
SBT
libraryDependencies += "org.apache.opennlp" % "opennlp-sandbox" % "${opennlp.version}"
Gradle
compile group: "org.apache.opennlp", name: "opennlp-sandbox", version: "${opennlp.version}"
For more details please check our documentation
Building OpenNLP
At least JDK 21 and Maven 3.3.9 are required to build the sandbox components.
After cloning the repository go into the destination directory and run:
mvn install
Contributing
The Apache OpenNLP project is developed by volunteers and is always looking for new contributors to work on all parts of the project. Every contribution is welcome and needed to make it better. A contribution can be anything from a small documentation typo fix to a new component.
If you would like to get involved please follow the instructions here