Nominalization and Argument Structure: An Experiment with the NOMLEX Database

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2022-8-31

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Kızıldemir, Melis

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This study presents a method for predicting syntactic structures of deverbal event nominals that are derived from verbs used in their transitive meaning. The study takes a data-driven approach and fine-tunes pre-trained deep learning models. The problem is treated as a 15 class multi-label classification task and NOMLEX is used to create the classes. In order to leverage the language models learned by pre-trained models, sentences available in corpora which include the verb that the deverbal nominal is derived from is used to fine-tune the models. As a result of this, selectional preferences of verbs are provided to the model implicitly. Sentences where the verb is used in its transitive meaning are filtered from the corpora using EasyCCG CCG parser. The DeBERTa model scores 66.4% in sample based accuracy and 77.9% in sample based F1score, and the RoBERTa model scores 75.0% in label based F1 score. When the models are evaluated on classes, at least one model performs better than baseline in 10 out of 15 classes. Deverbal nominal syntactic structures that only realize an argument in the possessive determiner position, two out of three syntactic structures that realize the subject in the posessive determiner position and two out of three syntactic structures that realize the subject in the noun modifier position are among unsuccessfully learned classes. It is concluded that determiners of an argument's realization in a syntactic position is dependent on both the argument and the syntactic position in question along with realization of other arguments. The presented method was able to learn these determiners to various extents.

Subject Keywords

nominalization, deep learning

URI

https://hdl.handle.net/11511/99517

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Graduate School of Informatics, Thesis

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Citation Formats

M. Kızıldemir, “Nominalization and Argument Structure: An Experiment with the NOMLEX Database,” M.S. - Master of Science, Middle East Technical University, 2022.