2012 TowardAutomatedDefinitionAcquis

Subject Headings: Definition Acquisition, Sentence Classification.

Notes

It explores the automation of definition acquisition from operations law for assisting military personnel.
It frames the process as a sentence classification task, addressed using machine learning techniques.
It reports high accuracy with supervised learning methods, achieving significant F1 and recall scores.
It addresses the challenge of manual data labeling by proposing a semi-supervised learning approach.
It provides insights into the balance between accuracy and efficiency in machine learning for legal applications.

Definition acquisition is a necessary step in building an artificial cognitive assistant that helps military personnel to gain fast and precise understanding of the various terms and procedures defined in applicable legal documents. We approach the task of identifying definitional sentences from operations law documents by formalizing this task as a sentence-classification task and solving it by using machine-learning methods. This paper reports on a series of empirical experiments in that we evaluate and compare the performance of learning algorithms in terms of label-prediction accuracy. Using supervised techniques results in an F1 score of 95.93% and a 96.75% recall rate. However, for real-world applications, it would be too costly and unrealistic to ask personnel involved in military operations to label substantial amounts of data in order to build a new classifier for different types or genres of text data. Therefore, we propose and implement a semi-supervised (SS) solution that trades off prediction accuracy to label efficiency. Our SS approach achieves a 90.47% F1 score and 93.44% recall rate by using only eight sentences labeled by a human expert.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 TowardAutomatedDefinitionAcquis	Yi Chang Jana Diesner Kathleen M. Carley			Toward Automated Definition Acquisition From Operations Law				10.1109/TSMCC.2011.2110643		2012