@inproceedings{choi-etal-2019-meta,
    title = "Meta-Semantic Representation for Early Detection of {A}lzheimer{'}s Disease",
    author = "Choi, Jinho D.  and
      Li, Mengmei  and
      Goldstein, Felicia  and
      Hajjar, Ihab",
    editor = "Xue, Nianwen  and
      Croft, William  and
      Hajic, Jan  and
      Huang, Chu-Ren  and
      Oepen, Stephan  and
      Palmer, Martha  and
      Pustejovksy, James",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-3309/",
    doi = "10.18653/v1/W19-3309",
    pages = "82--91",
    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."
}