BibTeX

@inProceedings{kokkinakis-etal-2016-specifications-243183,
	title        = {Specifications and Methodology for Language-Related Data Acquisition and Analysis in the Domain of Dementia Diagnostics},
	abstract     = {This paper outlines the initial stages of a project that aims to build and use a corpus with data samples acquired from people diagnosed with subjective or mild cognitive impairment and healthy, age-matched controls. The data we are currently collecting consists of audio-recorded spoken language samples; transcripts of the audio recordings and eye tracking measurements. From these data we plan to extract, evaluate and model features to be used for learning classification models in order to test how well a differentiation between the aforementioned subject groups can be made. Features will be also correlated with outcomes from e.g. other language-related scores, such as word fluency, in order to investigate whether there are relationships between various variables.},
	booktitle    = { The Sixth Swedish Language Technology Conference (SLTC) Umeå University, 17-18 November, 2016},
	author       = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina and Björkner, Eva and Nordlund, Arto},
	year         = {2016},
}

@inProceedings{kokkinakis-etal-2016-data-243069,
	title        = {Data Resource Acquisition from People at Various Stages of Cognitive Decline – Design and Exploration Considerations},
	abstract     = {In this paper we are introducing work in progress towards the development of an infrastructure (i.e., design, methodology, creation and description) of linguistic and extra-linguistic data samples acquired from people diagnosed with subjective or mild cognitive impairment and healthy, age-matched controls. The data we are currently collecting consists of various types of modalities; i.e. audio-recorded spoken language samples; transcripts of the audio recordings (text) and eye tracking measurements. The integration of the extra-linguistic information with the linguistic phenotypes and measurements elicited from audio and text, will be used to extract, evaluate and model features to be used in machine learning experiments. In these experiments, classification models that will be trained, that will be able to learn from the whole or a subset of the data to make predictions on new data in order to test how well a differentiation between the aforementioned groups can be made. Features will be also correlated with measured outcomes from e.g. language-related scores, such as word fluency, in order to investigate whether there are relationships between various variables.},
	booktitle    = {The Seventh International Workshop on Health Text Mining and Information Analysis (Louhi). November 5, 2016, Austin, Texas, USA},
	author       = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina and Nordlund, Arto},
	year         = {2016},
}

@inProceedings{lundholmfors-breitholtz-2016-mocking-240344,
	title        = {Are you mocking me or are you laughing with me?},
	booktitle    = { SEMDIAL 2016, JerSem, Proceedings of the 20th Workshop on the Semantics and Pragmatics of Dialogue, 16-18 July 2016 Rutgers, New Brunswick, NJ, USA /  Julie Hunter, Mandy Simons, and Matthew Stone (eds.)},
	author       = {Lundholm Fors, Kristina and Breitholtz, Ellen},
	year         = {2016},
}