文章基本信息

标题：Sentence Hedge Detection without Cue Annotation: A Heuristic Cue Selection Approach
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作者：André Kenji ; Kumiko Tanaka-Ishii
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2014
卷号：9
期号：2
页码：173-186
DOI：10.11185/imt.9.173
出版社：Information and Media Technologies Editorial Board
摘要：This paper presents a simple yet effective approach to sentence-level uncertainty detection which does not require cue word annotation. Unlike previous works, the proposed method focuses on cue selection, decoupling it from disambiguation and by optimizing it over sentence hedging error rate. High performance for the task is achieved in experiments, even for settings with poor disambiguation, without cue annotation and with otherwise unreliable corpora from a machine learning point-of-view.
关键词：Hedge Detection;Text Mining