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Annotating evidence-based argumentation in biomedical text

机译:在生物医学文本中注释循证论证

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A new challenge in natural language processing, argumentation mining is the automatic identification of an argument's premises, conclusion, and argumentation scheme, and relationships between arguments. Argumentation mining could provide critical context for information extraction and question answering and support new forms of summarization and citation indexing. However, argumentation mining, in this sense, has not yet been addressed in BioNLP. This theoretical paper contributes towards annotation of argumentation in biomedical/biological corpora. We show that argumentative zone models and models of discourse coherence do not represent the same aspects of discourse as a model of evidence-based argumentation. We explore the challenges of annotation of argumentation in full-text biomedical articles and describe the steps we have taken towards annotating a biomedical corpus for argumentation mining research.
机译:论语挖掘是自然语言处理中的一个新挑战,它是对论据的前提,结论和论证方案以及论据之间的关系的自动识别。论证挖掘可以为信息提取和问题解答提供关键上下文,并支持摘要和引文索引的新形式。但是,从这个意义上讲,论据挖掘尚未在BioNLP中得到解决。该理论论文有助于注释生物医学/生物学语料库中的论证。我们表明,论证区域模型和语篇连贯性模型与基于证据的论证模型所代表的话语并不相同。我们探讨了全文生物医学文章中论证注释的挑战,并描述了为论证挖掘研究注释生物医学语料库而采取的步骤。

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