@inproceedings{nagata-etal-2024-computational,
    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.",
}

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.
Nagata, Ryo Kawasaki, Yoshifumi Otani, Naoki Takamura, Hiroya
A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions