This paper presents a study based on the linguistic profiling methodology to explore the relationship between the linguistic structure of a text and how it is perceived in terms of writing quality by humans. The approach is tested on a selection of Italian L1 learners essays, which were taken from a larger longitudinal corpus of essays written by Italian L1 students enrolled in the first and second year of lower secondary school. Human ratings of writing quality by Italian native speakers were collected through a crowdsourcing task, in which annotators were asked to read pairs of essays and rated which one they believed to be better written. By analyzing these ratings, the study identifies a variety of linguistic phenomena spanning across distinct levels of linguistic description that distinguish the essays considered as 'winners' and evaluates the impact of students' errors on the human perception of writing quality.

Linguistic Profile of a Text and Human Ratings of Writing Quality: a Case Study on Italian L1 Learner Essays

Dominique Brunato;Felice Dell'Orletta
2023

Abstract

This paper presents a study based on the linguistic profiling methodology to explore the relationship between the linguistic structure of a text and how it is perceived in terms of writing quality by humans. The approach is tested on a selection of Italian L1 learners essays, which were taken from a larger longitudinal corpus of essays written by Italian L1 students enrolled in the first and second year of lower secondary school. Human ratings of writing quality by Italian native speakers were collected through a crowdsourcing task, in which annotators were asked to read pairs of essays and rated which one they believed to be better written. By analyzing these ratings, the study identifies a variety of linguistic phenomena spanning across distinct levels of linguistic description that distinguish the essays considered as 'winners' and evaluates the impact of students' errors on the human perception of writing quality.
2023
Istituto di linguistica computazionale "Antonio Zampolli" - ILC
human ratings
text quality
Natural Language Processing
learner corpus
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/455146
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