OpenMinTeD Catalogue of TDM Components

Find TDM and NLP components and mix them together

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The catalogue of Text and Data Mining (TDM) components brings together pieces of software that perform basic TDM and Natural Language Processing (NLP) tasks and can be mixed together to build sophisticated applications. All components have been adapted to the OpenMinTeD interoperability specifications which aim at enabling their integration irrespective of their original implementation framework and ensure that they can be executed without problems in the OpenMinTeD platform. The catalogue targets mainly TDM developers, i.e. expert users who know how to combine them appropriately in the same workflow and build ready-to-run applications. The service is empowered with browse and search (faceted and google-like free text) functionalities based on the harmonised metadata descriptions of the resources. For each component, the user can view: (a) administrative information, such as its title, a short description, licence or terms of use, provenance information (resource creator, creation dates, funding programs, etc.); (b) technical information, such as the task it performs, the requirements on the input resource (e.g. whether it runs on a corpus of English, on texts with a specific data format, etc.), details on the output it produces, links to documents that may help the users (e.g. user manuals, video tutorials, publications about the component, etc.), etc. StandardThe main asset of this catalogue is that it brings together basic TDM and NLP software components conforming to the OpenMinTeD interoperability specifications and are, thus, guaranteed to be executed without problems in the platform and easy to be combined with other components.

Scientific categorisation
  • Generic
    • Generic
  • Data Analysis
    • Other
Target users
  • Researchers
Resource availability and languages
  • English
More about OpenMinTeD Catalogue of TDM Components

The EOSC portal is been jointly developed and maintained by the EOSC-hub, eInfraCentral and OpenAIRE-Advance projects funded by the European

Union’s Horizon 2020 research and innovation programme with contribution of the European Commission

2018 EOSC Portal