Improving POS Tagging of German Learner Language in a Reading Comprehension Scenario
نویسندگان
چکیده
We present a novel method to automatically improve the accurrcy of part-of-speech taggers on learner language. The key idea underlying our approach is to exploit the structure of a typical language learner task and automatically induce POS information for out-of-vocabulary (OOV) words. To evaluate the effectiveness of our approach, we add manual POS and normalization information to an existing language learner corpus. Our evaluation shows an increase in accuracy from 72.4% to 81.5% on OOV words.
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