一种跨体裁包围的鲁棒Reddit词性标注方法
A Cross-Genre Ensemble Approach to Robust Reddit Part of Speech Tagging
摘要：
词性标注是一项基本的NLP任务，通常被认为是为英语等高资源语言解决的。目前最先进的模型已经达到了很高的准确性，特别是在新闻领域。然而，当这些模型应用于其他不同类型的语料库，特别是来自Web的用户生成的数据时，我们看到性能大幅下降。在这项工作中，我们研究了一个在不同流派上训练的最先进的标记模型如何在未经过滤的Reddit论坛讨论的Web内容上执行。更具体地说，我们使用来自多个来源的数据：OntoNotes，一个大型的基准语料库，具有“精心编辑”的文本，英语Web Treebank具有5种Web类型，以及GUM，除了Reddit之外还有7种类型。我们报告了在Reddit上测试的不同数据分割上训练的结果。我们的研究结果表明，即使是少量的域内数据可以胜过贡献的数据的数量级更大的来自其他Web域。为了在域外标记方面取得进展，我们还评估了一种集成方法，该方法使用多个单一流派的标记作为元分类器的输入特征。我们介绍了标记Reddit数据的最新性能，以及对这些模型结果的错误分析，并提供了其中最常见的错误类型的类型学，按训练语料库进行分类。
英文摘要：
Part of speech tagging is a fundamental NLP task often regarded as solved for high-resource languages such as English. Current state-of-the-art models have achieved high accuracy, especially on the news domain. However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance. In this work, we study how a state-of-the-art tagging model trained on different genres performs on Web content from unfiltered Reddit forum discussions. More specifically, we use data from multiple sources: OntoNotes, a large benchmark corpus with 'well-edited' text, the English Web Treebank with 5 Web genres, and GUM, with 7 further genres other than Reddit. We report the results when training on different splits of the data, tested on Reddit. Our results show that even small amounts of in-domain data can outperform the contribution of data an order of magnitude larger coming from other Web domains. To make progress on out-of-domain tagging, we also evaluate an ensemble approach using multiple single-genre taggers as input features to a meta-classifier. We present state of the art performance on tagging Reddit data, as well as error analysis of the results of these models, and offer a typology of the most common error types among them, broken down by training corpus.
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。
