Detecting negation scope is easy, except when it isn’t

Federico Fancellu, Adam Lopez, Bonnie Webber, Hangfeng He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Several corpora have been annotated with negation scope—the set of words whose meaning is negated by a cue like the word “not”—leading to the development of classifiers that detect negation scope with high accuracy. We show that for nearly all of these corpora, this high accuracy can be attributed to a single fact: they frequently annotate negation scope as a single span of text delimited by punctuation. For negation scopes not of this form, detection accuracy is low and undersampling the easy training examples does not substantially improve accuracy. We demonstrate that this is partly an artifact of annotation guidelines, and we argue that future negation scope annotation efforts should focus on these more difficult cases.
Original languageEnglish
Title of host publicationProceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
PublisherAssociation for Computational Linguistics (ACL)
Number of pages6
ISBN (Print)978-1-945626-34-0
Publication statusPublished - 7 Apr 2017
Event15th EACL 2017 Software Demonstrations - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017


Conference15th EACL 2017 Software Demonstrations
Abbreviated titleEACL 2017
Internet address

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