---
title: "A broad-coverage collection of portable NLP components for"
description: "The authors present DKPro Core, a UIMA-based collection that wraps a wide range of third-party NLP… 31 references, cited by 66 papers here."
authors: ["Richard Eckart de Castilho", "Iryna Gurevych"]
year: 2014
venue: "Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks for HLT"
source: "https://aclanthology.org/W14-5201/"
doi: "10.3115/v1/w14-5201"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/castilho-2014-broad-coverage-collection-portable-nlp/"
---
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# A broad-coverage collection of portable NLP components for building shareable analysis pipelines

**Authors:** [Richard Eckart de Castilho](https://md.research.elamur.ai/free/en/authors/richard-eckart-de-castilho/) and [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) · **Year:** 2014 · **Venue:** Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks for HLT · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W14-5201/) · [PDF](https://aclanthology.org/W14-5201.pdf) · [DOI 10.3115/v1/w14-5201](https://doi.org/10.3115/v1/w14-5201) · [OpenAlex](https://openalex.org/W2251991755)

**Citations:** 130 citations (OpenAlex, 2026-10-14) · [66](https://md.research.elamur.ai/free/en/papers/castilho-2014-broad-coverage-collection-portable-nlp/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Building NLP pipelines from heterogeneous tools is difficult, and sharing a finished pipeline is harder still. Pipeline descriptors in GATE and Apache UIMA refer to components by name and to resources often only by file name, with no version information. A recipient must therefore rebuild the environment manually, and results may not be reproducible. The authors present DKPro Core, a UIMA-based collection that wraps a wide range of third-party NLP tools as interoperable components. Service-oriented platforms such as WebLicht, Argo, U-Compare and AnnoMarket rely on remote services. DKPro Core instead ships only portable software, distributed through Maven repositories. Its main novelty is an auto-configuration mechanism: at runtime, a component chooses a fitting resource (such as a model) and fetches it from the repository. As a result, a complete pipeline can be written as a short Groovy script whose dependencies download automatically, so it runs on any machine with Groovy installed. This supports reproducibility, teaching, documentation and work on clusters or sensitive local data. Users can also keep backups of every artifact.

## Key points
- DKPro Core is built on Apache UIMA. Components and resources are published as Maven artifacts, uniquely addressed by groupId, artifactId and version, through the federated Maven network with the Central Repository as the default entry point.
- The authors find gaps in existing infrastructure:
  - GATE and UIMA XML descriptors lack versions.
  - The META-SHARE and CLARIN VLO directories target humans and lack data for programmatic download.
  - WebLicht (with its TCF format), Argo and U-Compare depend largely on web services or closed platforms.
- The paper separates two tasks:
  - **Resource selection:** choosing a resource and configuring a component with it.
  - **Resource acquisition:** obtaining that resource.
  
  Both are implemented independently of the processing framework.
- The demonstration is a self-contained Groovy script (Listing 1). Jython with jip, or Java with Maven, would also work. The collection has been used for preprocessing in temporal tagging, topic-model-based text segmentation and textual entailment.
- **Known limitations:**
  - Tagsets and tokenization are passed through without checking.
  - Automatic selection can pick a POS model whose tagset does not match the following parser.
  - Future work plans finer, metadata-based selection to replace the "variant" coordinate.

## Interesting ideas
- **Version models like code.** Trained models and other resources can be published as versioned artifacts in the same package infrastructure used for code. This would let a team version, cache and archive models exactly like libraries.
- **Separate choosing a resource from fetching it.** Splitting these two steps lets components default to a sensible resource while allowing explicit overrides. This keeps pipeline configurations short without hiding which resource was actually used.
- **Make the pipeline description executable.** A small runnable script that pulls its own dependencies works as both documentation and a reproducibility record. It suits papers, tutorials, Q&A answers and internal onboarding.
- **Keep the resolver pluggable.** Repository infrastructures and coordinate schemes may change over time, so the lookup mechanism should be swappable. Rich catalogue metadata could then be combined with automatic downloading, or even with service calls.
- **Check compatibility between chained components.** When a pipeline links components trained under different conventions, it is worth automatically detecting mismatched tagsets or tokenization. Such silent mismatches degrade results without raising any error.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Technische Universität Darmstadt](https://md.research.elamur.ai/free/en/universities/technische-universitat-darmstadt/) — University, Darmstadt, Germany · 76 works · 35,008 citations
- [DIPF | Leibniz Institute for Research and Information in Education](https://md.research.elamur.ai/free/en/labs/dipf-leibniz-institute-for-research-and-information-in-education/) — Research institute, Frankfurt am Main, Germany · 8 works · 2,199 citations

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## Cite

Cite as: Richard Eckart de Castilho and Iryna Gurevych. 2014. A broad-coverage collection of portable NLP components for building shareable analysis pipelines. Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks for HLT.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{castilho2014broad,
  title = {{A broad-coverage collection of portable NLP components for building shareable analysis pipelines}},
  author = {Richard Eckart {de Castilho} and Iryna Gurevych},
  booktitle = {Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks for HLT},
  year = {2014},
  doi = {10.3115/v1/w14-5201},
  url = {https://doi.org/10.3115/v1/w14-5201}
}
```

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*Updated 24 October 2026*