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双語会話におけるMCI検出のための解釈可能な言語特性の統計分析
Arezo Shakeri1, Mina Farmanbar1
1Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway.
Alzheimer's & dementia (Amsterdam, Netherlands)
|February 12, 2026
まとめ
軽度の認知障害 (MCI) の早期発見は極めて重要です. この研究では,英語と中国語の間でMCIを確実に示すバイリンガルスピーチの特定の言語的特徴を特定し,多言語スクリーニングツールへの道を開きました.
科学分野:
- 神経科学は神経科学である.
- コンピュータ言語学 コンピュータ言語学
- 精神言語学は,心理言語学である.
背景:
- 軽度の認知障害 (MCI) の早期発見は,適切な介入と認知症の予防に不可欠です.
- 解釈可能な言語マーカーは,MCI検出のバイリンガルな文脈では十分に研究されていない.
- バイリンガルなデータセットは,言語間の認知マーカーを特定するユニークな機会を提供します.
研究 の 目的:
- 双言語のデータセットでMCIに関連する解釈可能な言語特性を特定する.
- MCI検出のための言語特異的および言語不可知性の特徴を探求する.
- 多言語MCI評価ツールを開発するための言語間の基盤を確立する.
主な方法:
- TAUKADIALチャレンジのデータセットを利用し,英語と中国語の画像説明を使用しました.
- 自然言語データセットのための効率的な言語特性抽出 (ELFEN) パッケージを使用して93の言語特有の特徴を抽出しました.
- Comprehensive Handcrafted Linguistic Features (LFTK) ツールキットを使用して141の言語アグノスティック特性を抽出し,統計分析 (ANOVA,Tukey's HSD) を行いました.
主要な成果:
- 7つのELFENと33のLFTKの特徴は,診断グループ間の有意な違いを示した.
- 軽度の認知障害 (MCI) のスピーチは,語彙の多様性が低下し,代名詞が少なく,数字と分詞の使用が増加し,両方の言語でより長い文が特徴でした.
- 分析により,構造的および語彙的変動が明らかになり,中国の回答はより大きな変動を示しました.
結論:
- MCIに関連した統計的に有意で解釈可能な言語的特徴を特定しました.
- 英語と中国語の絵の記述に一貫したクロス言語マーカーを確立しました.
- 透明で多言語のMCIスクリーニングツールを作成するための検証された基盤を提供しました.
キーワード:
アノバ・アノバ (ANOVA) とはアルツハイマー病 (Alzheimer's disease) とは,アルツハイマー病 (Alzheimer's disease) とは,アルツハイマー病 (Alzheimer'sコグニティブスクリーニングのスクリーニング言語的特徴 言語的特徴 言語的特徴軽度の認知機能障害である.スピーチ スピーチ スピーチさらに関連する動画
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