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    <abstract>This paper presents a new task-oriented meaning representation called meta-semantics, that is designed to detect patients with early symptoms of Alzheimer‘s disease by analyzing their language beyond a syntactic or semantic level. Meta-semantic representation consists of three parts, entities, predicate argument structures, and discourse attributes, that derive rich knowledge graphs. For this study, 50 controls and 50 patients with mild cognitive impairment (MCI) are selected, and meta-semantic representation is annotated on their speeches transcribed in text. Inter-annotator agreement scores of 88%, 82%, and 89% are achieved for the three types of annotation, respectively. Five analyses are made using this annotation, depicting clear distinctions between the control and MCI groups. Finally, a neural model is trained on features extracted from those analyses to classify MCI patients from normal controls, showing a high accuracy of 82% that is very promising.</abstract>
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%0 Conference Proceedings
%T Meta-Semantic Representation for Early Detection of Alzheimer‘s Disease
%A Choi, Jinho D.
%A Li, Mengmei
%A Goldstein, Felicia
%A Hajjar, Ihab
%Y Xue, Nianwen
%Y Croft, William
%Y Hajic, Jan
%Y Huang, Chu-Ren
%Y Oepen, Stephan
%Y Palmer, Martha
%Y Pustejovksy, James
%S Proceedings of the First International Workshop on Designing Meaning Representations
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F choi-etal-2019-meta
%X This paper presents a new task-oriented meaning representation called meta-semantics, that is designed to detect patients with early symptoms of Alzheimer‘s disease by analyzing their language beyond a syntactic or semantic level. Meta-semantic representation consists of three parts, entities, predicate argument structures, and discourse attributes, that derive rich knowledge graphs. For this study, 50 controls and 50 patients with mild cognitive impairment (MCI) are selected, and meta-semantic representation is annotated on their speeches transcribed in text. Inter-annotator agreement scores of 88%, 82%, and 89% are achieved for the three types of annotation, respectively. Five analyses are made using this annotation, depicting clear distinctions between the control and MCI groups. Finally, a neural model is trained on features extracted from those analyses to classify MCI patients from normal controls, showing a high accuracy of 82% that is very promising.
%R 10.18653/v1/W19-3309
%U https://aclanthology.org/W19-3309/
%U https://doi.org/10.18653/v1/W19-3309
%P 82-91

Markdown (Informal)

[Meta-Semantic Representation for Early Detection of Alzheimer’s Disease](https://aclanthology.org/W19-3309/) (Choi et al., DMR 2019)

• Meta-Semantic Representation for Early Detection of Alzheimer’s Disease (Choi et al., DMR 2019)

ACL

• Jinho D. Choi, Mengmei Li, Felicia Goldstein, and Ihab Hajjar. 2019. Meta-Semantic Representation for Early Detection of Alzheimer’s Disease. In Proceedings of the First International Workshop on Designing Meaning Representations, pages 82–91, Florence, Italy. Association for Computational Linguistics.