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Rhetorical Move Detection in English s: Multi-label Sentence Classifiers and their Annotated Corpora

机译:英语S:多标签句子分类器及其注释语料库的修辞运动检测

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The relevance of automatically identifying rhetorical moves in scientific texts has been widely acknowledged in the literature. This study focuses on abstracts of standard research papers written in English and aims to tackle a fundamental limitation of current machine-learning classifiers: they are mono-labeled, that is, a sentence can only be assigned one single label. However, such approach does not adequately reflect actual language use since a move can be realized by a clause, a sentence, or even several sentences. Here, we present MAZEA (Multi-label Argumentative Zoning for English Abstracts), a multi-label classifier which automatically identifies rhetorical moves in abstracts but allows for a given sentence to be assigned as many labels as appropriate. We have resorted to various other NLP tools and used two large training corpora: (ⅰ) one corpus consists of 645 abstracts from physical sciences and engineering (PE) and (ⅱ) the other corpus is made up of 690 from life and health sciences (LH). This paper presents our preliminary results and also discusses the various challenges involved in multi-label tagging and works towards satisfactory solutions. In addition, we also make our two training corpora publicly available so that they may serve as benchmark for this new task.
机译:在文献中,自动识别科学文本中的修辞运动的相关性已被广泛承认。本研究侧重于用英语编写的标准研究论文的摘要,并旨在解决当前机器学习分类器的基本限制:它们是单声标,即句子只能分配一个单一标签。然而,这种方法不会充分反映实际语言使用,因为一个句话可以由一个句子,句子或甚至几个句子实现。在这里,我们展示了Mazea(多标签争论分区用于英语摘要),这是一个多标签分类器,它自动识别摘要中的修辞移动,但允许给定句子根据适当地分配适当的标签。我们采取了各种其他NLP工具,并使用了两种大型培训:(Ⅰ)一个语料库由物理科学和工程(PE)和(Ⅱ)其他语料库由690次来自生命和健康科学( LH)。本文介绍了我们的初步结果,还讨论了多标签标签中涉及的各种挑战,并符合令人满意的解决方案。此外,我们还将我们的两个培训集团公开提供,以便他们可以作为这项新任务的基准。

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