Supersense tagging with inter-annotator disagreement
Supersense tagging with inter-annotator disagreement
复制标题
具有注释者间分歧的超义标记
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Barbara Plank
中科院分区：
文献类型：
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作者：
Héctor Martínez Alonso;Anders Johannsen;Barbara Plank
语言标注是自然语言处理（NLP）中许多成功方法的基础，其中标注的语料库用于训练和评估监督学习者。注释的一致性限制了监督模型的性能，因此大量的努力被投入到获得高一致性的注释数据集上。最近的研究表明，注释不一致不是随机噪声，而是一个系统信号，可用于改进监督学习器。然而，以前的工作是有限的范围内，只集中在一种语言的词性标注。在本文中，我们扩大了实验的语义任务（supersense标记）使用多种语言。特别是，我们分析了如何系统的分歧是意义注释，我们提出了一个初步的研究，是否模式的分歧跨语言的转移。
Linguistic annotation underlies many successful approaches in Natural Language Processing (NLP), where the annotated corpora are used for training and evaluating supervised learners. The consistency of annotation limits the performance of supervised models, and thus a lot of effort is put into obtaining high-agreement annotated datasets. Recent research has shown that annotation disagreement is not random noise, but carries a systematic signal that can be used for improving the supervised learner. However, prior work was limited in scope, focusing only on part-of-speech tagging in a single language. In this paper we broaden the experiments to a semantic task (supersense tagging) using multiple languages. In particular, we analyse how systematic disagreement is for sense annotation, and we present a preliminary study of whether patterns of disagreements transfer across languages.
回复：“使用数值方法设计模拟：重新审视平衡截距”。
影响因子：
5
作者：
Zivich,PaulN;Ross,RachaelK
通讯作者：
Ross,RachaelK