@inproceedings{yavas-etal-2023-identifying,
title = "Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach",
author = "Yavas, Deniz Ekin and
Kallmeyer, Laura and
Osswald, Rainer and
Jezek, Elisabetta and
Ricchiardi, Marta and
Chen, Long",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-1.35",
pages = "310--320",
abstract = "Identifying semantic argument types in predication contexts is not a straightforward task for several reasons, such as inherent polysemy, coercion, and copredication phenomena. In this paper, we train monolingual and multilingual classifiers with a zero-shot cross-lingual approach to identify semantic argument types in predications using pre-trained language models as feature extractors. We train classifiers for different semantic argument types and for both verbal and adjectival predications. Furthermore, we propose a method to detect copredication using these classifiers through identifying the argument semantic type targeted in different predications over the same noun in a sentence. We evaluate the performance of the method on copredication test data with Food{\mbox{$\bullet$}}Event nouns for 5 languages.",
}
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<abstract>Identifying semantic argument types in predication contexts is not a straightforward task for several reasons, such as inherent polysemy, coercion, and copredication phenomena. In this paper, we train monolingual and multilingual classifiers with a zero-shot cross-lingual approach to identify semantic argument types in predications using pre-trained language models as feature extractors. We train classifiers for different semantic argument types and for both verbal and adjectival predications. Furthermore, we propose a method to detect copredication using these classifiers through identifying the argument semantic type targeted in different predications over the same noun in a sentence. We evaluate the performance of the method on copredication test data with Food\bulletEvent nouns for 5 languages.</abstract>
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%0 Conference Proceedings
%T Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach
%A Yavas, Deniz Ekin
%A Kallmeyer, Laura
%A Osswald, Rainer
%A Jezek, Elisabetta
%A Ricchiardi, Marta
%A Chen, Long
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F yavas-etal-2023-identifying
%X Identifying semantic argument types in predication contexts is not a straightforward task for several reasons, such as inherent polysemy, coercion, and copredication phenomena. In this paper, we train monolingual and multilingual classifiers with a zero-shot cross-lingual approach to identify semantic argument types in predications using pre-trained language models as feature extractors. We train classifiers for different semantic argument types and for both verbal and adjectival predications. Furthermore, we propose a method to detect copredication using these classifiers through identifying the argument semantic type targeted in different predications over the same noun in a sentence. We evaluate the performance of the method on copredication test data with Food\bulletEvent nouns for 5 languages.
%U https://aclanthology.org/2023.ranlp-1.35
%P 310-320
Markdown (Informal)
[Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach](https://aclanthology.org/2023.ranlp-1.35) (Yavas et al., RANLP 2023)
ACL
- Deniz Ekin Yavas, Laura Kallmeyer, Rainer Osswald, Elisabetta Jezek, Marta Ricchiardi, and Long Chen. 2023. Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 310–320, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.