@inproceedings{occhipinti-2024-enhancing,
title = "Enhancing Lexical Complexity Prediction in {I}talian through Automatic Morphological Segmentation",
author = "Occhipinti, Laura",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.75/",
pages = "670--678",
ISBN = "979-12-210-7060-6",
abstract = "Morphological analysis is vital for various NLP tasks as it provides insights into word structures and enhances the understanding of morphological and syntactic relationships. This study focuses on surface morphological segmentation for the Italian language, addressing the lack of detailed morphological representation in existing corpora. By utilizing an automatic segmenter, we aim to extract quantitative morphological parameters to understand their impact on word complexity perception. Our correlation analysis reveals that morphological features significantly influence the perceived complexity of words."
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%0 Conference Proceedings
%T Enhancing Lexical Complexity Prediction in Italian through Automatic Morphological Segmentation
%A Occhipinti, Laura
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F occhipinti-2024-enhancing
%X Morphological analysis is vital for various NLP tasks as it provides insights into word structures and enhances the understanding of morphological and syntactic relationships. This study focuses on surface morphological segmentation for the Italian language, addressing the lack of detailed morphological representation in existing corpora. By utilizing an automatic segmenter, we aim to extract quantitative morphological parameters to understand their impact on word complexity perception. Our correlation analysis reveals that morphological features significantly influence the perceived complexity of words.
%U https://aclanthology.org/2024.clicit-1.75/
%P 670-678
Markdown (Informal)
[Enhancing Lexical Complexity Prediction in Italian through Automatic Morphological Segmentation](https://aclanthology.org/2024.clicit-1.75/) (Occhipinti, CLiC-it 2024)
ACL