@inproceedings{sedinkina-etal-2019-automatic,
title = "Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes",
author = {Sedinkina, Marina and
Breitkopf, Nikolas and
Sch{\"u}tze, Hinrich},
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/P19-1034/",
doi = "10.18653/v1/P19-1034",
pages = "346--359",
abstract = "In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (i) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert`s a priori belief about a word`s meaning can be incorrect {--} annotation should be performed based on the word`s contexts in the target domain instead."
}
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%0 Conference Proceedings
%T Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
%A Sedinkina, Marina
%A Breitkopf, Nikolas
%A Schütze, Hinrich
%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 sedinkina-etal-2019-automatic
%X In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (i) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert‘s a priori belief about a word‘s meaning can be incorrect – annotation should be performed based on the word‘s contexts in the target domain instead.
%R 10.18653/v1/P19-1034
%U https://aclanthology.org/P19-1034/
%U https://doi.org/10.18653/v1/P19-1034
%P 346-359
Markdown (Informal)
[Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes](https://aclanthology.org/P19-1034/) (Sedinkina et al., ACL 2019)
ACL