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    <abstract>This paper investigates the semantic prosody of three causal connectives: due to, owing to and because of in seven varieties of the English language. While research in the domain of English causality exists, we are not aware of studies that would cover the domain of causal connectives in English. Our claim is that connectives such as because of link two arguments, (at least) one of which will include a phrase that contributes to the interpretation of the relation as positive or negative, and hence define the prosody of the connective used. As our results demonstrate, the majority of the prosodies identified are negative for all three connectives; the proportions are stable across the varieties of English studied, and contrary to our expectations, we find no significant differences between the functions of the connectives and discourse preferences. Further, we investigate whether automatizing the sentiment annotation procedure via a simple language-model based classifier is possible. The initial results highlights the complexity of the task and the need for complicated systems, probably aided with other related datasets to achieve reasonable performance.</abstract>
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%0 Conference Proceedings
%T A Sentiment-annotated Dataset of English Causal Connectives
%A Andersson, Marta
%A Kurfalı, Murathan
%A Östling, Robert
%S Proceedings of the 14th Linguistic Annotation Workshop
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain
%F andersson-etal-2020-sentiment
%X This paper investigates the semantic prosody of three causal connectives: due to, owing to and because of in seven varieties of the English language. While research in the domain of English causality exists, we are not aware of studies that would cover the domain of causal connectives in English. Our claim is that connectives such as because of link two arguments, (at least) one of which will include a phrase that contributes to the interpretation of the relation as positive or negative, and hence define the prosody of the connective used. As our results demonstrate, the majority of the prosodies identified are negative for all three connectives; the proportions are stable across the varieties of English studied, and contrary to our expectations, we find no significant differences between the functions of the connectives and discourse preferences. Further, we investigate whether automatizing the sentiment annotation procedure via a simple language-model based classifier is possible. The initial results highlights the complexity of the task and the need for complicated systems, probably aided with other related datasets to achieve reasonable performance.
%U https://aclanthology.org/2020.law-1.3
%P 24-33

Markdown (Informal)

[A Sentiment-annotated Dataset of English Causal Connectives](https://aclanthology.org/2020.law-1.3) (Andersson et al., LAW 2020)

• A Sentiment-annotated Dataset of English Causal Connectives (Andersson et al., LAW 2020)

ACL

• Marta Andersson, Murathan Kurfalı, and Robert Östling. 2020. A Sentiment-annotated Dataset of English Causal Connectives. In Proceedings of the 14th Linguistic Annotation Workshop, pages 24–33, Barcelona, Spain. Association for Computational Linguistics.