评估语言文档中的自动化策略

Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing - HLT '09 Pub Date : 2009-06-05 DOI:10.3115/1564131.1564139

Alexis Palmer, Taesun Moon, Jason Baldridge

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引用次数: 22

摘要

本文介绍了将机器标记和主动学习与人为注释数据相结合的试点工作，用于为玛雅语Uspanteko创建互线性注释文本(IGT)的语言文档任务。实际目标是生成一个完全注释的语料库，在有限的时间内尽可能准确地进行手动注释。我们描述了正在进行的试点研究，这些研究检查了三个主要因素对减少注释IGT所花费时间的影响:来自机器标注器的建议、样本选择方法和注释器专业知识。

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Evaluating Automation Strategies in Language Documentation

This paper presents pilot work integrating machine labeling and active learning with human annotation of data for the language documentation task of creating interlinearized gloss text (IGT) for the Mayan language Uspanteko. The practical goal is to produce a totally annotated corpus that is as accurate as possible given limited time for manual annotation. We describe ongoing pilot studies which examine the influence of three main factors on reducing the time spent to annotate IGT: suggestions from a machine labeler, sample selection methods, and annotator expertise.

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Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing - HLT '09

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