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arXiv 1503.01190cs.CLcs.LGstat.ML
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基于规则注释和众包的统计模态标记

Statistical modality tagging from rule-based annotations and crowdsourcing

Columbia University（哥伦比亚大学）；University of Maryland（马里兰大学）；Carnegie Mellon University（卡内基梅隆大学）；Johns Hopkins University（约翰斯·霍普金斯大学）

Vinodkumar Prabhakaran, Michael Bloodgood, Mona Diab, Bonnie Dorr, Lori Levin, Christine D. Piatko, Owen Rambow, Benjamin Van Durme

备注：
8 pages, 6 tables; appeared in Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, July 2012; In Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, pages 57-64, Jeju, Republic of Korea, July 2012. Association for Computational Linguistics
发表信息：
In Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, pages 57-64, Jeju, Republic of Korea, July 2012. Association for Computational Linguistics
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摘要：

我们探索训练自动模态标注器。情态是说话人对某一事件或状态的态度。训练语言标记器的主要障碍之一是收集训练数据。这对于训练用于模态的标记器来说尤其成问题，因为模态触发器对于绝大多数句子来说是稀疏的。我们研究了一种自动训练模态标注器的方法，我们首先根据一个高召回率的简单的基于规则的模态标注器收集句子，然后将这些句子提供给Mechanical Turk注释器进行进一步注释。我们使用得到的一组训练数据训练一个精确的模态标签使用多类SVM，提供良好的性能。

英文摘要：

We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for the overwhelming majority of sentences. We investigate an approach to automatically training a modality tagger where we first gathered sentences based on a high-recall simple rule-based modality tagger and then provided these sentences to Mechanical Turk annotators for further annotation. We used the resulting set of training data to train a precise modality tagger using a multi-class SVM that delivers good performance.

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。

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