Automatic Identification of Rhetorical Questions
Automatic Identification of Rhetorical Questions
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自动识别反问句
DOI：
10.3115/v1/p15-2122
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Joonsuk Park
中科院分区：
文献类型：
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作者：
Shohini Bhattasali;Jeremy Cytryn;Elana Feldman;Joonsuk Park
提问不仅可以引出信息，也可以陈述观点。服务于后一个目的的问题，称为修辞问题，通常在词汇和句法上与其他类型的问题没有区别。尽管如此，仍然希望能够识别反问句，因为它与许多NLP任务相关，包括信息提取和文本摘要。在本文中，我们探讨了在很大程度上研究不足的问题，反问句识别。具体来说，我们提出了一个简单的基于n-gram的语言模型来分类的交换机对话行为语料库中的反问句。我们发现，一个特殊的处理修辞问题，结合上下文信息实现了最高的性能。
A question may be asked not only to elicit information, but also to make a statement. Questions serving the latter purpose, called rhetorical questions, are often lexically and syntactically indistinguishable from other types of questions. Still, it is desirable to be able to identify rhetorical questions, as it is relevant for many NLP tasks, including information extraction and text summarization. In this paper, we explore the largely understudied problem of rhetorical question identification. Specifically, we present a simple n-gram based language model to classify rhetorical questions in the Switchboard Dialogue Act Corpus. We find that a special treatment of rhetorical questions which incorporates contextual information achieves the highest performance.