Learning Lexico-Functional Patterns for First-Person Affect

Lena Reed, Jiaqi Wu, Shereen Oraby, Pranav Anand, Marilyn Walker


Abstract
Informal first-person narratives are a unique resource for computational models of everyday events and people’s affective reactions to them. People blogging about their day tend not to explicitly say I am happy. Instead they describe situations from which other humans can readily infer their affective reactions. However current sentiment dictionaries are missing much of the information needed to make similar inferences. We build on recent work that models affect in terms of lexical predicate functions and affect on the predicate’s arguments. We present a method to learn proxies for these functions from first-person narratives. We construct a novel fine-grained test set, and show that the patterns we learn improve our ability to predict first-person affective reactions to everyday events, from a Stanford sentiment baseline of .67F to .75F.
Anthology ID:
P17-2022
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
141–147
Language:
URL:
https://aclanthology.org/P17-2022
DOI:
10.18653/v1/P17-2022
Bibkey:
Cite (ACL):
Lena Reed, Jiaqi Wu, Shereen Oraby, Pranav Anand, and Marilyn Walker. 2017. Learning Lexico-Functional Patterns for First-Person Affect. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 141–147, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Learning Lexico-Functional Patterns for First-Person Affect (Reed et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2022.pdf
Video:
 https://aclanthology.org/P17-2022.mp4