This section refers you to all the components of the OpeNER framework.
Make sure to checkout the Quick Start Guide.


Input Tools

This collection of components is used to start OpeNER pipelines. For now a language identifier is available.


Combined this group of components delivers you state of the art Named Entity Recognition and Named Entity Disambiguation.


These components can store results of opener pipelines into the database or on amazon s3. You can use them as webservices, or use them as examples on how to build your own storage solutions.

Processors / Aggregators

These components aggregate, change, calculate or change the KAF document in any way. Where most other components have a KAF-in KAF-out policy the processors will output completely different formats.


These components are the start of each OpeNER pipeline. Every further operation needs tokens, polarities and lemmas. For your convenience multiple POS taggers are available.

Polarity, Properties and Opinions

Combined these components deliver you top notch opinion detection on texts in general, as well as on detected properties. The basic detector is rule based, and the deluxe detector is machine learned.


KAF is a nice format, but there are others. JSON for example is well known, and often hooks into nice javascript visualisations. NAF is more like KAF evolved, used in several other NLP projects.


While making all other components, we created several tools to annotate texts and to adapt components for different languages and domains. The most useful tools are listed below.


The OpeNER project produces some interesting Lexicons for you to use. You can find more information on the lexicons by following the links below.


Several modules of the OpeNER project as well as some demos have been trained and created using several datasets. You can find information about those datasets by following the links below.


OpeNER heavily relies on KAF and OpeNER LMF. We also provide a translation of KAF into JSON. Below you can find the reference documentation for each.

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 261712.