Papers by Anne-Kathrin Schumann

2 papers
Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems (2020.lrec-1)

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Challenge: Existing sentiment data sets are not available for sentiment analysis.
Approach: They propose to combine a German sentiment corpus with existing resources to train a general-purpose German sentiment classification model.
Outcome: The proposed model trains a general-purpose German sentiment classification model . the data set contains 5.4 million labelled samples .
Automatic Annotation of Semantic Term Types in the Complete ACL Anthology Reference Corpus (L18-1)

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Challenge: a recent increase in quantitative studies of scientific text collections has led to a significant increase in the use of semantic labeling techniques.
Approach: They propose to use semantic class labels to enhance a well-known resource . they use semantic labels to assign semantic class labeling to technical terms .
Outcome: The proposed approach enhances the ACL Anthology Reference Corpus with semantic class labels for 20,000 technical terms . the goal is to use this information as one feature in the profiling of scientific papers, communities, and disciplines.

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