@inproceedings{reed-etal-2017-learning,
title = "Learning Lexico-Functional Patterns for First-Person Affect",
author = "Reed, Lena and
Wu, Jiaqi and
Oraby, Shereen and
Anand, Pranav and
Walker, Marilyn",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2022",
doi = "10.18653/v1/P17-2022",
pages = "141--147",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Learning Lexico-Functional Patterns for First-Person Affect
%A Reed, Lena
%A Wu, Jiaqi
%A Oraby, Shereen
%A Anand, Pranav
%A Walker, Marilyn
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F reed-etal-2017-learning
%X 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.
%R 10.18653/v1/P17-2022
%U https://aclanthology.org/P17-2022
%U https://doi.org/10.18653/v1/P17-2022
%P 141-147
Markdown (Informal)
[Learning Lexico-Functional Patterns for First-Person Affect](https://aclanthology.org/P17-2022) (Reed et al., ACL 2017)
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.