Active learning for sense annotation
نویسندگان
چکیده
This article describes a real (nonsynthetic) active-learning experiment to obtain supersense annotations for Danish. We compare two instance selection strategies, namely lowest-prediction confidence (MAX), and sampling from the confidence distribution (SAMPLE). We evaluate their performance during the annotation process, across domains for the final resulting system, as well as against in-domain adjudicated data. The SAMPLE strategy yields competitive models that are more robust than the overly length-biased selection criterion of MAX.
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