Minimally-Supervised Morphological Segmentation using Adaptor Grammars
Minimally-Supervised Morphological Segmentation using Adaptor Grammars
复制标题
使用适配器语法的最小监督形态分割
DOI：
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发表时间：
2013
影响因子：
10.9
通讯作者：
S. Goldwater
中科院分区：
文献类型：
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作者：
Kairit Sirts;S. Goldwater
本文探讨了使用适配器语法（一种非参数贝叶斯建模框架）进行最低限度监督的形态分割。我们比较了三种训练方法：无监督训练、半监督训练和一种新颖的模型选择方法。在模型选择方法中，我们使用过度表达的元语法来训练无监督的适配器语法，然后使用小型标记数据集来选择应在最终输出中返回由元语法识别的潜在变形边界。我们对五种语言进行了评估，结果表明半监督训练比无监督训练有所提升，而模型选择方法在所有语言中产生了最好的平均结果，并且与最先进的半监督系统具有竞争力。此外，该方法提供了根据不同的评估指标或下游任务调整性能的潜力。
This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.