Using Hedge Detection to Improve Committed Belief Tagging

Morgan Ulinski, Seth Benjamin, Julia Hirschberg


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.
Anthology ID:
W18-1301
Volume:
Proceedings of the Workshop on Computational Semantics beyond Events and Roles
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
NAACL | SemBEaR | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–5
Language:
URL:
https://aclanthology.org/W18-1301
DOI:
10.18653/v1/W18-1301
Bibkey:
Cite (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.
Cite (Informal):
Using Hedge Detection to Improve Committed Belief Tagging (Ulinski et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-1301.pdf