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--- |
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tags: |
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- flair |
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- token-classification |
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- sequence-tagger-model |
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language: de |
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--- |
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# REDEWIEDERGABE Tagger: free indirect STWR |
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This model is part of an ensemble of binary taggers that recognize German speech, thought and writing representation. They can be used to automatically detect and annotate the following 4 types of speech, thought and writing representation in German texts: |
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| STWR type | Example | Translation | |
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|--------------------------------|-------------------------------------------------------------------------|----------------------------------------------------------| |
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| direct | Dann sagte er: **"Ich habe Hunger."** | Then he said: **"I'm hungry."** | |
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| free indirect ('erlebte Rede', **this tagger**) | Er war ratlos. **Woher sollte er denn hier bloß ein Mittagessen bekommen?** | He was at a loss. **Where should he ever find lunch here?** | |
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| indirect | Sie fragte, **wo das Essen sei.** | She asked **where the food was.** | |
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| reported | **Sie sprachen über das Mittagessen.** | **They talked about lunch.** | |
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The ensemble is trained on the [REDEWIEDERGABE corpus](https://github.com/redewiedergabe/corpus) ([Annotation guidelines](http://redewiedergabe.de/richtlinien/richtlinien.html)), fine-tuning each tagger on the domain-adapted [lkonle/fiction-gbert-large](https://huggingface.co/lkonle/fiction-gbert-large). ([Training Code](https://github.com/cophi-wue/LLpro/blob/main/contrib/train_redewiedergabe.py)) |
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**F1-Scores:** |
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| STWR type | F1-Score | |
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|-----------|-----------| |
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| direct | 90.76 | |
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| indirect | 79.16 | |
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| **free indirect (this tagger)** | **58.00** | |
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| reported | 70.47 | |
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---- |
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**Demo Usage:** |
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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sentence = Sentence('Sie sprachen über das Mittagessen. Sie fragte, wo das Essen sei. Woher sollte er das wissen? Dann sagte er: "Ich habe Hunger."') |
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rwtypes = ['direct', 'indirect', 'freeindirect', 'reported'] |
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for rwtype in rwtypes: |
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model = SequenceTagger.load(f'aehrm/redewiedergabe-{rwtype}') |
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model.predict(sentence) |
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print(rwtype, [ x.data_point.text for x in sentence.get_labels() ]) |
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# >>> direct ['"', 'Ich', 'habe', 'Hunger', '.', '"'] |
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# >>> indirect ['wo', 'das', 'Essen', 'sei', '.'] |
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# >>> freeindirect ['Woher', 'sollte', 'er', 'das', 'wissen', '?'] |
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# >>> reported ['Sie', 'sprachen', 'über', 'das', 'Mittagessen', '.', 'Woher', 'sollte', 'er', 'das', 'wissen', '?'] |
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``` |
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**Cite**: |
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Please cite the following paper when using this model. |
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``` |
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@inproceedings{ehrmanntraut-et-al-llpro-2023, |
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address = {Ingolstadt, Germany}, |
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title = {{LLpro}: A Literary Language Processing Pipeline for {German} Narrative Text}, |
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booktitle = {Proceedings of the 10th Conference on Natural Language Processing ({KONVENS} 2022)}, |
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publisher = {{KONVENS} 2023 Organizers}, |
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author = {Ehrmanntraut, Anton and Konle, Leonard and Jannidis, Fotis}, |
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year = {2023}, |
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} |
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``` |
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