Added README for gnfinder v2

duui-gnfinder-v2/README.md

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+# HoToUse
+For using GNfinder as a DUUI image it is necessary to use the Docker Unified UIMA Interface.
+
+## Use as Stand-Alone-Image
+```bash
+docker run docker.texttechnologylab.org/duui-gnfinder-v2:latest
+```
+
+## Run with a specific port
+```bash
+docker run -p 1000:9714 docker.texttechnologylab.org/duui-gnfinder-v2:latest
+```
+
+## Run within DUUI
+```java
+composer.add(new DUUIDockerDriver.
+    Component("docker.texttechnologylab.org/duui-gnfinder-v2:latest")
+    .withScale(iWorkers)
+    .withImageFetching());
+```
+
+## Existing Parameters
+
+| Parameter | Description | Datatype | Default |
+| --- | --- | --- | --- |
+| language | Language of the text for better detection accuracy. | String | detect |
+| ambiguousNames | Include ambiguous name matches. | Boolean | False |
+| noBayes | Disable Bayesian odds calculation. | Boolean | False |
+| oddsDetails | Include detailed odds calculation information. | Boolean | False |
+| verification | Enable verification of found names against data sources. | Boolean | True |
+| sources | List of data source IDs to use for verification. | String | [11] |
+| allMatches | Return all matches instead of only the best match. | Boolean | False |
+
+
+# Cite
+If you want to use the DUUI image please quote this as follows:
+
+
+Alexander Leonhardt, Giuseppe Abrami, Daniel Baumartz and Alexander Mehler. (2023). "Unlocking the Heterogeneous Landscape of Big Data NLP with DUUI." Findings of the Association for Computational Linguistics: EMNLP 2023, 385–399. [[LINK](https://aclanthology.org/2023.findings-emnlp.29)] [[PDF](https://aclanthology.org/2023.findings-emnlp.29.pdf)] 
+
+## BibTeX
+```
+@inproceedings{Leonhardt:et:al:2023,
+  title     = {Unlocking the Heterogeneous Landscape of Big Data {NLP} with {DUUI}},
+  author    = {Leonhardt, Alexander and Abrami, Giuseppe and Baumartz, Daniel and Mehler, Alexander},
+  editor    = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
+  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
+  year      = {2023},
+  address   = {Singapore},
+  publisher = {Association for Computational Linguistics},
+  url       = {https://aclanthology.org/2023.findings-emnlp.29},
+  pages     = {385--399},
+  pdf       = {https://aclanthology.org/2023.findings-emnlp.29.pdf},
+  abstract  = {Automatic analysis of large corpora is a complex task, especially
+               in terms of time efficiency. This complexity is increased by the
+               fact that flexible, extensible text analysis requires the continuous
+               integration of ever new tools. Since there are no adequate frameworks
+               for these purposes in the field of NLP, and especially in the
+               context of UIMA, that are not outdated or unusable for security
+               reasons, we present a new approach to address the latter task:
+               Docker Unified UIMA Interface (DUUI), a scalable, flexible, lightweight,
+               and feature-rich framework for automatic distributed analysis
+               of text corpora that leverages Big Data experience and virtualization
+               with Docker. We evaluate DUUI{'}s communication approach against
+               a state-of-the-art approach and demonstrate its outstanding behavior
+               in terms of time efficiency, enabling the analysis of big text
+               data.}
+}
+
+@misc{Abrami:2022,
+  author         = {Abrami, Giuseppe},
+  title          = {GNfinder as DUUI-Komponent},
+  year           = {2022},
+  howpublished   = {https://github.com/texttechnologylab/duui-uima/edit/main/duui-GNFinder}
+}
+
+```