利用从语言样本中提取的词汇特征自动识别阿尔茨海默病
Automatic Identification of Alzheimer's Disease using Lexical Features extracted from Language Samples
AI总结：
本文基于ADDreSS语料提取99个词汇特征并训练分类器，实现AD语言样本超过91%的F1与准确率，还通过作家文本和里根演讲验证泛化性。
摘要：
目的：本研究具有双重目标。首先，旨在增进对阿尔茨海默病（AD）型痴呆对词汇不同方面影响的理解。其次，旨在证明词汇的这些方面在用作机器学习分类器的特征时，能够有助于在自动识别AD患者产生的语言样本方面取得最先进的性能。方法：数据来源于ADDreSS挑战赛，它是DementiaBank语料库的一部分。所使用的数据集由“偷饼干”图片描述的转写文本构成，训练部分由54名受试者产生，测试部分由24名受试者产生。叙事样本数量在训练集中为108个，在测试集中为48个。首先，使用数据集的训练和测试两部分研究AD对99个选定词汇特征的影响。随后，在将AD患者产生的语音转写样本和文本样本与正常受试者产生的样本进行分类的任务上开展了若干机器学习实验。研究进行了多项实验，以比较词汇复杂性的不同领域，识别有助于实现最佳性能的特征子集，并研究输入规模对分类的影响。为评估基于叙事言语构建的模型的泛化能力，研究使用两位英国作家Iris Murdoch和Agatha Christie的书面数据，以及前总统Ronald Reagan部分演讲的转写文本开展了两项泛化测试。结果：仅使用词汇特征，在区分AD个体与健康对照受试者产生的语言样本时，取得了F1和准确率均超过91%的最先进分类结果。这证实了AD对词汇加工的实质性影响。
英文摘要：
Objective: this study has a twofold goal. First, it aims to improve the understanding of the impact of Dementia of type Alzheimer's Disease (AD) on different aspects of the lexicon. Second, it aims to demonstrate that such aspects of the lexicon, when used as features of a machine learning classifier, can help achieve state-of-the-art performance in automatically identifying language samples produced by patients with AD. Methods: data is derived from the ADDreSS challenge, which is a part of the DementiaBank corpus. The used dataset consists of transcripts of Cookie Theft picture descriptions, produced by 54 subjects in the training part and 24 subjects in the test part. The number of narrative samples is 108 in the training set and 48 in the test set. First, the impact of AD on 99 selected lexical features is studied using both the training and testing parts of the dataset. Then some machine learning experiments were conducted on the task of classifying transcribed speech samples with text samples that were produced by people with AD from those produced by normal subjects. Several experiments were conducted to compare the different areas of lexical complexity, identify the subset of features that help achieve optimal performance, and study the impact of the size of the input on the classification. To evaluate the generalization of the models built on narrative speech, two generalization tests were conducted using written data from two British authors, Iris Murdoch and Agatha Christie, and the transcription of some speeches by former President Ronald Reagan. Results: using lexical features only, state-of-the-art classification, F1 and accuracies, of over 91% were achieved in categorizing language samples produced by individuals with AD from the ones produced by healthy control subjects. This confirms the substantial impact of AD on lexicon processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
