@inproceedings{stepisnik-perdih-etal-2022-sentiment,
title = "Sentiment Classification by Incorporating Background Knowledge from Financial Ontologies",
author = "Stepi{\v{s}}nik-Perdih, Timen and
Pelicon, Andra{\v{z}} and
{\v{S}}krlj, Bla{\v{z}} and
{\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and
Lon{\v{c}}arski, Igor and
Pollak, Senja",
editor = "El-Haj, Mahmoud and
Rayson, Paul and
Zmandar, Nadhem",
booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.fnp-1.3",
pages = "17--26",
abstract = "Ontologies are increasingly used for machine reasoning over the last few years. They can provide explanations of concepts or be used for concept classification if there exists a mapping from the desired labels to the relevant ontology. This paper presents a practical use of an ontology for the purpose of data set generalization in an oversampling setting, with the aim of improving classification models. We demonstrate our solution on a novel financial sentiment data set using the Financial Industry Business Ontology (FIBO). The results show that generalization-based data enrichment benefits simpler models in a general setting and more complex models such as BERT in low-data setting.",
}
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<abstract>Ontologies are increasingly used for machine reasoning over the last few years. They can provide explanations of concepts or be used for concept classification if there exists a mapping from the desired labels to the relevant ontology. This paper presents a practical use of an ontology for the purpose of data set generalization in an oversampling setting, with the aim of improving classification models. We demonstrate our solution on a novel financial sentiment data set using the Financial Industry Business Ontology (FIBO). The results show that generalization-based data enrichment benefits simpler models in a general setting and more complex models such as BERT in low-data setting.</abstract>
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%0 Conference Proceedings
%T Sentiment Classification by Incorporating Background Knowledge from Financial Ontologies
%A Stepišnik-Perdih, Timen
%A Pelicon, Andraž
%A Škrlj, Blaž
%A Žnidaršič, Martin
%A Lončarski, Igor
%A Pollak, Senja
%Y El-Haj, Mahmoud
%Y Rayson, Paul
%Y Zmandar, Nadhem
%S Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F stepisnik-perdih-etal-2022-sentiment
%X Ontologies are increasingly used for machine reasoning over the last few years. They can provide explanations of concepts or be used for concept classification if there exists a mapping from the desired labels to the relevant ontology. This paper presents a practical use of an ontology for the purpose of data set generalization in an oversampling setting, with the aim of improving classification models. We demonstrate our solution on a novel financial sentiment data set using the Financial Industry Business Ontology (FIBO). The results show that generalization-based data enrichment benefits simpler models in a general setting and more complex models such as BERT in low-data setting.
%U https://aclanthology.org/2022.fnp-1.3
%P 17-26
Markdown (Informal)
[Sentiment Classification by Incorporating Background Knowledge from Financial Ontologies](https://aclanthology.org/2022.fnp-1.3) (Stepišnik-Perdih et al., FNP 2022)
ACL