@inproceedings{nagata-etal-2024-computational-approach,
title = "A Computational Approach to Quantifying Grammaticization of {E}nglish Deverbal Prepositions",
author = "Nagata, Ryo and
Kawasaki, Yoshifumi and
Otani, Naoki and
Takamura, Hiroya",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.19",
pages = "211--220",
abstract = "This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as {``}regarding{''} and {``}according to{''}. Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. This paper augments them by methods for measuring the degree of grammaticization of deverbal prepositions based on non-contextualized or contextualized word vectors. Experiments show that the methods correlate well with human judgements (as high as 0.69 in Spearman{'}s rank correlation coefficient). Using the best-performing method, this paper further shows that the methods support previous findings in linguistics including (i) Deverbal prepositions are marginal in terms of prepositionality; and (ii) The process where verbs are grammaticized into prepositions is gradual. As a pilot study, it also conducts a diachronic analysis of grammaticization of deverbal preposition.",
}
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%0 Conference Proceedings
%T A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions
%A Nagata, Ryo
%A Kawasaki, Yoshifumi
%A Otani, Naoki
%A Takamura, Hiroya
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F nagata-etal-2024-computational-approach
%X This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as “regarding” and “according to”. Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. This paper augments them by methods for measuring the degree of grammaticization of deverbal prepositions based on non-contextualized or contextualized word vectors. Experiments show that the methods correlate well with human judgements (as high as 0.69 in Spearman’s rank correlation coefficient). Using the best-performing method, this paper further shows that the methods support previous findings in linguistics including (i) Deverbal prepositions are marginal in terms of prepositionality; and (ii) The process where verbs are grammaticized into prepositions is gradual. As a pilot study, it also conducts a diachronic analysis of grammaticization of deverbal preposition.
%U https://aclanthology.org/2024.lrec-main.19
%P 211-220
Markdown (Informal)
[A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions](https://aclanthology.org/2024.lrec-main.19) (Nagata et al., LREC-COLING 2024)
ACL