Unravelling interlanguage facts via explainable machine learning.
Native language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the performance of NLI systems has steadily improved over the years....
| Publicado en: | Digital Scholarship in the Humanities Vol. 38; no. 3; pp. 953 - 978 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Sep2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=171389429&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 171389429 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Sep2023 vid: 38 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 171389429 10.1093/llc/fqad019 ppf: 953 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P size: 807KB tig: atl: Unravelling interlanguage facts via explainable machine learning. aug: au: Berti, Barbara Esuli, Andrea Sebastiani, Fabrizio affil: Dipartimento di Lingue, Letterature, Culture e Mediazioni, Università degli Studi di Milano , Milano, Italy Istituto di Scienza e Tecnologie dell'Informazione, Consiglio Nazionale delle Ricerche , Pisa, Italy su: Machine learning Supervised learning Native language Linguistics English language sug: subj: Machine learning Supervised learning Native language Linguistics English language ab: Native language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the performance of NLI systems has steadily improved over the years. We focus on a different facet of the NLI task, i.e. that of analysing the internals of an NLI classifier trained by an explainable machine learning (EML) algorithm, in order to obtain explanations of its classification decisions, with the ultimate goal of gaining insight into which linguistic phenomena 'give a speaker's native language away'. We use this perspective in order to tackle both NLI and a (much less researched) companion task, i.e. guessing whether a text has been written by a native or a non-native speaker. Using three datasets of different provenance (two datasets of English learners' essays and a dataset of social media posts), we investigate which kind of linguistic traits (lexical, morphological, syntactic, and statistical) are most effective for solving our two tasks, namely, are most indicative of a speaker's L1; our experiments indicate that the most discriminative features are the lexical ones, followed by the morphological, syntactic, and statistical features, in this order. We also present two case studies, one on Italian and one on Spanish learners of English, in which we analyse individual linguistic traits that the classifiers have singled out as most important for spotting these L1s; we show that the traits identified as most discriminative well align with our intuition, i.e. represent typical patterns of language misuse, underuse, or overuse, by speakers of the given L1. Overall, our study shows that the use of EML can be a valuable tool for the scholar who investigates interlanguage facts and language transfer. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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