Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to as- sess single words lexical complexity, combin- ing linguistic and psycholinguistic variables in a set of experiments involving random forest and XGboost regressors. Beyond encoding out-of-context information about the lemma, we implemented features based on pre-trained language models to model the target word’s in-context complexity.
archer at SemEval-2021 task 1: Contextualising lexical complexity
irene russo
Primo
2021
Abstract
Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to as- sess single words lexical complexity, combin- ing linguistic and psycholinguistic variables in a set of experiments involving random forest and XGboost regressors. Beyond encoding out-of-context information about the lemma, we implemented features based on pre-trained language models to model the target word’s in-context complexity.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.orgunit | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | en |
| dc.authority.people | irene russo | en |
| dc.collection.id.s | 71c7200a-7c5f-4e83-8d57-d3d2ba88f40d | * |
| dc.collection.name | 04.01 Contributo in Atti di convegno | * |
| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
| dc.contributor.appartenenza.mi | 918 | * |
| dc.date.accessioned | 2024/12/17 16:13:45 | - |
| dc.date.available | 2024/12/17 16:13:45 | - |
| dc.date.firstsubmission | 2024/10/07 14:38:41 | * |
| dc.date.issued | 2021 | - |
| dc.date.submission | 2024/10/07 14:38:41 | * |
| dc.description.abstracteng | Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to as- sess single words lexical complexity, combin- ing linguistic and psycholinguistic variables in a set of experiments involving random forest and XGboost regressors. Beyond encoding out-of-context information about the lemma, we implemented features based on pre-trained language models to model the target word’s in-context complexity. | - |
| dc.description.allpeople | Russo, Irene | - |
| dc.description.allpeopleoriginal | irene russo | en |
| dc.description.fulltext | open | en |
| dc.description.international | no | en |
| dc.description.numberofauthors | 1 | - |
| dc.identifier.source | manual | * |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/505402 | - |
| dc.language.iso | eng | en |
| dc.relation.conferencename | 15th International Workshop on Semantic Evaluation (SemEval-2021) | en |
| dc.relation.firstpage | 694 | en |
| dc.relation.ispartofbook | Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) | en |
| dc.relation.lastpage | 699 | en |
| dc.relation.numberofpages | 6 | en |
| dc.subject.keywordseng | lexical complexity | - |
| dc.subject.singlekeyword | lexical complexity | * |
| dc.title | archer at SemEval-2021 task 1: Contextualising lexical complexity | en |
| dc.type.driver | info:eu-repo/semantics/conferenceObject | - |
| dc.type.full | 04 Contributo in convegno::04.01 Contributo in Atti di convegno | it |
| dc.type.invited | contributo | en |
| dc.type.miur | 273 | - |
| iris.mediafilter.data | 2025/04/15 04:29:40 | * |
| iris.orcid.lastModifiedDate | 2024/12/17 16:13:45 | * |
| iris.orcid.lastModifiedMillisecond | 1734448425495 | * |
| iris.sitodocente.maxattempts | 1 | - |
| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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