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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