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            <title>Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)</title>
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        <name type="personal">
            <namePart type="given">Nikolai</namePart>
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    <abstract>Negation constitutes a challenging phenomenon for many natural language processing tasks, such as sentiment analysis (SA). In this paper we investigate the relationship between negation and sentiment in the context of Norwegian professional reviews. The first part of this paper includes a corpus study which investigates how negation is tied to sentiment in this domain, based on existing annotations. In the second part, we introduce NoReC-NegSynt, a synthetically augmented test set for negation and sentiment, to allow for a more detailed analysis of the role of negation in current neural SA models. This diagnostic test set, containing both clausal and non-clausal negation, allows for analyzing and comparing models’ abilities to treat several different types of negation. We also present a case-study, applying several neural SA models to the diagnostic data.</abstract>
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%0 Conference Proceedings
%T A Diagnostic Dataset for Sentiment and Negation Modeling for Norwegian
%A Mæhlum, Petter
%A Velldal, Erik
%A Øvrelid, Lilja
%Y Ilinykh, Nikolai
%Y Morger, Felix
%Y Dannélls, Dana
%Y Dobnik, Simon
%Y Megyesi, Beáta
%Y Nivre, Joakim
%S Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Tórshavn, the Faroe Islands
%F maehlum-etal-2023-diagnostic
%X Negation constitutes a challenging phenomenon for many natural language processing tasks, such as sentiment analysis (SA). In this paper we investigate the relationship between negation and sentiment in the context of Norwegian professional reviews. The first part of this paper includes a corpus study which investigates how negation is tied to sentiment in this domain, based on existing annotations. In the second part, we introduce NoReC-NegSynt, a synthetically augmented test set for negation and sentiment, to allow for a more detailed analysis of the role of negation in current neural SA models. This diagnostic test set, containing both clausal and non-clausal negation, allows for analyzing and comparing models’ abilities to treat several different types of negation. We also present a case-study, applying several neural SA models to the diagnostic data.
%U https://aclanthology.org/2023.resourceful-1.11/
%P 77-85

Markdown (Informal)

[A Diagnostic Dataset for Sentiment and Negation Modeling for Norwegian](https://aclanthology.org/2023.resourceful-1.11/) (Mæhlum et al., RESOURCEFUL 2023)

• A Diagnostic Dataset for Sentiment and Negation Modeling for Norwegian (Mæhlum et al., RESOURCEFUL 2023)

ACL

• Petter Mæhlum, Erik Velldal, and Lilja Øvrelid. 2023. A Diagnostic Dataset for Sentiment and Negation Modeling for Norwegian. In Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023), pages 77–85, Tórshavn, the Faroe Islands. Association for Computational Linguistics.