Automated morphological analysis of clinical language samples.

Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting Pub Date : 2015-06-05

Kyle Gorman, Steven Bedrick, Géza Kiss, Eric Morley, Rosemary Ingham, Metrah Mohammad, Katina Papadakis, Jan P H van Santen

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Abstract

Quantitative analysis of clinical language samples is a powerful tool for assessing and screening developmental language impairments, but requires extensive manual transcription, annotation, and calculation, resulting in error-prone results and clinical underutilization. We describe a system that performs automated morphological analysis needed to calculate statistics such as the mean length of utterance in morphemes (MLUM), so that these statistics can be computed directly from orthographic transcripts. Estimates of MLUM computed by this system are closely comparable to those produced by manual annotation. Our system can be used in conjunction with other automated annotation techniques, such as maze detection. This work represents an important first step towards increased automation of language sample analysis, and towards attendant benefits of automation, including clinical greater utilization and reduced variability in care delivery.

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临床语言样本的自动形态学分析。

临床语言样本的定量分析是评估和筛查发育性语言障碍的有力工具，但需要大量的人工转录、注释和计算，导致结果容易出错和临床未充分利用。我们描述了一个执行自动形态学分析所需的系统，以计算统计数据，如语素中的平均话语长度(MLUM)，以便这些统计数据可以直接从正字法转录本中计算出来。该系统计算的MLUM估计值与人工标注的估计值接近。我们的系统可以与其他自动标注技术结合使用，比如迷宫检测。这项工作是朝着提高语言样本分析自动化迈出的重要的第一步，也是朝着自动化带来的好处迈出的重要的第一步，包括临床更大的利用率和减少医疗服务的可变性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting

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