Papers by Ronja Laarmann-Quante

2 papers
LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language (2022.lrec-1)

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Challenge: Existing spellcheckers do not work well with learner data.
Approach: They propose a multi-lingual evaluation data set of spelling mistakes in context that is highly customizable for the DKPro architecture.
Outcome: The proposed spellchecker improves performance in many settings and can be customized to meet learners' needs.
Automatic Extraction of Nominal Phrases from German Learner Texts of Different Proficiency Levels (2024.lrec-main)

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Challenge: a pilot study has found that inflecting determiners and adjectives correctly is a challenge for learners of German.
Approach: They propose to use dependency parsing to extract nouns, grammatical heads and dependents that have to agree with the noun in German.
Outcome: The proposed method performs well on CEFR levels A1-B1 but not level B2 texts.

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