论文标题
出色的功能和在哪里可以找到它们：通过子序列分类检测认知障碍
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach
论文作者
论文摘要
尽管基于嵌入式的机器学习方法在自然语言处理任务上有广泛报道，但在认知障碍（CI）检测等领域中，使用更容易解释的工程特征的使用仍然很常见。嘈杂文本的手动工程功能是时间和资源消耗，并且可能导致无法增强模型性能的功能。为了打击这一问题，我们描述了一种新的方法来特征工程，该方法利用了连续的机器学习模型和域知识来预测哪些功能有助于提高性能。我们在CI语音的标准数据集上提供了该方法的具体示例，并证明使用该方法产生的功能时，CI分类精度在强基线上提高了2.3％。该演示提供了一个前样本，介绍了如何使用该方法来协助解释性重要的领域（例如医疗保健）的分类。
Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains common in fields such as cognitive impairment (CI) detection. Manually engineering features from noisy text is time and resource consuming, and can potentially result in features that do not enhance model performance. To combat this, we describe a new approach to feature engineering that leverages sequential machine learning models and domain knowledge to predict which features help enhance performance. We provide a concrete example of this method on a standard data set of CI speech and demonstrate that CI classification accuracy improves by 2.3% over a strong baseline when using features produced by this method. This demonstration provides an ex-ample of how this method can be used to assist classification in fields where interpretability is important, such as health care.
