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        <title>Using Hedge Detection to Improve Committed Belief Tagging</title>
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    <abstract>We describe a novel method for identifying hedge terms using a set of manually constructed rules. We present experiments adding hedge features to a committed belief system to improve classification. We compare performance of this system (a) without hedging features, (b) with dictionary-based features, and (c) with rule-based features. We find that using hedge features improves performance of the committed belief system, particularly in identifying instances of non-committed belief and reported belief.</abstract>
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%0 Conference Proceedings
%T Using Hedge Detection to Improve Committed Belief Tagging
%A Ulinski, Morgan
%A Benjamin, Seth
%A Hirschberg, Julia
%Y Blanco, Eduardo
%Y Morante, Roser
%S Proceedings of the Workshop on Computational Semantics beyond Events and Roles
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F ulinski-etal-2018-using
%X We describe a novel method for identifying hedge terms using a set of manually constructed rules. We present experiments adding hedge features to a committed belief system to improve classification. We compare performance of this system (a) without hedging features, (b) with dictionary-based features, and (c) with rule-based features. We find that using hedge features improves performance of the committed belief system, particularly in identifying instances of non-committed belief and reported belief.
%R 10.18653/v1/W18-1301
%U https://aclanthology.org/W18-1301
%U https://doi.org/10.18653/v1/W18-1301
%P 1-5

Markdown (Informal)

[Using Hedge Detection to Improve Committed Belief Tagging](https://aclanthology.org/W18-1301) (Ulinski et al., SemBEaR 2018)

• Using Hedge Detection to Improve Committed Belief Tagging (Ulinski et al., SemBEaR 2018)

ACL

• Morgan Ulinski, Seth Benjamin, and Julia Hirschberg. 2018. Using Hedge Detection to Improve Committed Belief Tagging. In Proceedings of the Workshop on Computational Semantics beyond Events and Roles, pages 1–5, New Orleans, Louisiana. Association for Computational Linguistics.