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引用次数: 3
摘要
在本文中,我们描述了我们在EVALITA 2020 KIPoS共享任务(Bosco et al., 2020)的背景下进行的意大利语口语词性标注实验。我们提交的共享任务是基于SoMeWeTa (Proisl, 2018),这是一个支持领域自适应的标记器,旨在灵活地整合外部资源。我们记录我们的方法,并在共享任务中讨论我们的结果,同时对影响性能最大的因素进行统计分析。此外,我们报告了一组涉及神经语言模型与无监督hmm相结合的附加实验,并将其性能与我们的系统进行了比较。
KLUMSy @ KIPoS: Experiments on Part-of-Speech Tagging of Spoken Italian
In this paper, we describe experiments on part-of-speech tagging of spoken Italian that we conducted in the context of the EVALITA 2020 KIPoS shared task (Bosco et al., 2020). Our submission to the shared task is based on SoMeWeTa (Proisl, 2018), a tagger which supports domain adaptation and is designed to flexibly incorporate external resources. We document our approach and discuss our results in the shared task along with a statistical analysis of the factors which impact performance the most. Additionally, we report on a set of additional experiments involving the combination of neural language models with unsupervised HMMs, and compare its performance to that of our system.