@inproceedings{field-tsvetkov-2019-entity,
title = "Entity-Centric Contextual Affective Analysis",
author = "Field, Anjalie and
Tsvetkov, Yulia",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1243",
doi = "10.18653/v1/P19-1243",
pages = "2550--2560",
abstract = "While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.",
}
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<abstract>While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.</abstract>
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%0 Conference Proceedings
%T Entity-Centric Contextual Affective Analysis
%A Field, Anjalie
%A Tsvetkov, Yulia
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F field-tsvetkov-2019-entity
%X While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.
%R 10.18653/v1/P19-1243
%U https://aclanthology.org/P19-1243
%U https://doi.org/10.18653/v1/P19-1243
%P 2550-2560
Markdown (Informal)
[Entity-Centric Contextual Affective Analysis](https://aclanthology.org/P19-1243) (Field & Tsvetkov, ACL 2019)
ACL
- Anjalie Field and Yulia Tsvetkov. 2019. Entity-Centric Contextual Affective Analysis. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2550–2560, Florence, Italy. Association for Computational Linguistics.