Papers by Nicklas Linz

2 papers
The Metalogue Debate Trainee Corpus: Data Collection and Annotations (L18-1)

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Challenge: Argumentation is an important component of human intelligence and is used to train lawyers and citizens in legal domains.
Approach: They describe the Metalogue Debate Trainee Corpus (DTC) which contains data on motion and speech capture devices and semantic annotations.
Outcome: The metalogue Debate Trainee Corpus (DTC) was developed to facilitate the design of instructional and interactive models for the Virtual Debate Coach application.
Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling (N19-1)

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Challenge: Existing work on speech and language models has been limited by the size of available datasets.
Approach: They propose to augment a small French dataset with a much larger English dataset to augment the language model to model the order in which information units are produced by dementia patients and controls.
Outcome: The proposed model improves classification performance in English and French separately.

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