Papers by Frank Fischer

3 papers
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains (P19-1)

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Challenge: Existing deep learning methods require large amounts of training data to achieve reasonable performance.
Approach: They propose to generate automatic annotation suggestions for a discourse-level sequence labelling task that requires extensive domain expertise.
Outcome: The proposed model improves with newly annotated texts while introducing no biases.
“A Buster Keaton of Linguistics”: First Automated Approaches for the Extraction of Vossian Antonomasia (D19-1)

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Challenge: Vossian Antonomasia is a stylistic device which attributes a property to a person by naming another person as a reference point.
Approach: They propose a method for the extraction of Vossian Antonomasias that works completely automatically . they use named entity recognition, distant supervision and a bi-directional LSTM .
Outcome: The proposed method outperforms the only existing semi-automatic method for VA identification by more than 30 percentage points in precision.
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning (D19-3)

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Challenge: Existing systems for technologyenhanced learning address skills on recalling, explaining, and applying knowledge, e.g., in automatically generated language learning exercises and math word problems.
Approach: They propose to leverage a NLP model to support experts in their further data annotation with automatic suggestions and provide automatic feedback for students.
Outcome: The proposed system improves on two user studies on diagnostic reasoning in medicine and teacher education and can be extended to further use cases.

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