Move references to the end of the readme
Change-Id: Ide0d608fccda1dfd5775707e49952e2b21f37065
diff --git a/Readme.md b/Readme.md
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+++ b/Readme.md
@@ -8,27 +8,6 @@
fast natural language tokenization, based on a finite state
transducer generated with [Foma](https://fomafst.github.io/).
-## References
-
-Please cite this work as:
-
-> Diewald, N. (2022): *Matrix and Double-Array Representations
-> for Efficient Finite State Tokenization*. In: Proceedings of the
-> 10th Workshop on Challenges in the Management of Large Corpora
-> (CMLC-10) at LREC 2022. Marseille, France.
-
-The library contains sources for a german tokenizer
-based on [KorAP-Tokenizer](https://github.com/KorAP/KorAP-Tokenizer).
-
-For speed and quality analysis in comparison to other tokenizers for German,
-please refer to this article:
-
-> Diewald, N./Kupietz, M./Lüngen, H. (2022): *Tokenizing on scale -
-> Preprocessing large text corpora on the lexical and sentence level*.
-> In: Proceedings of EURALEX 2022. Mannheim, Germany.
-
-The benchmarks can be reproduced using [this test suite](https://github.com/KorAP/Tokenizer-Evaluation).
-
## Tokenization
```
@@ -173,6 +152,29 @@
et al. (2000) and implementation details following Kanda et al. (2018).
+## References
+
+Please cite this work as:
+
+> Diewald, Nils (2022): *Matrix and Double-Array Representations
+> for Efficient Finite State Tokenization*. In: Proceedings of the
+> 10th Workshop on Challenges in the Management of Large Corpora
+> (CMLC-10) at LREC 2022. Marseille, France.
+
+The library contains sources for a german tokenizer
+based on [KorAP-Tokenizer](https://github.com/KorAP/KorAP-Tokenizer).
+
+For speed and quality analysis in comparison to other tokenizers for German,
+please refer to this article:
+
+> Diewald, Nils, Marc Kupietz, Harald Lüngen (2022): *Tokenizing on scale -
+> Preprocessing large text corpora on the lexical and sentence level*.
+> In: Proceedings of EURALEX 2022. Mannheim, Germany.
+
+The benchmarks can be reproduced using
+[this test suite](https://github.com/KorAP/Tokenizer-Evaluation).
+
+
## License
Datok is published under the [Apache 2.0 License](LICENSE).