katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English

Ekaterina Voskoboinik

doi:10.18653/v1/2021.semeval-1.91

katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English

Ekaterina Voskoboinik

Speech Recognition

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

2 Sitaatiot (Scopus)

4 Lataukset (Pure)

Abstrakti

This paper describes systems submitted to SemEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a nonlinear regression models trained to work for both tracks of the task. We show that both systems are able to generalize better when supplied with information about complexities of single word and multi-word expression (MWE) targets simultaneously. This approach proved to be the most beneficial for multi-word expression targets. We also demonstrate that some hand-crafted features differ in their importance for the target types.

Alkuperäiskieli	Englanti
Otsikko	SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop
Toimittajat	Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
Kustantaja	Association for Computational Linguistics
Sivut	700-705
Sivumäärä	6
ISBN (elektroninen)	9781954085701
DOI - pysyväislinkit	https://doi.org/10.18653/v1/2021.semeval-1.91
Tila	Julkaistu - 2021
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	International Workshop on Semantic Evaluation - Virtual, Bangkok, Thaimaa Kesto: 5 elok. 2021 → 6 elok. 2021

Julkaisusarja

Nimi	SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop

Workshop

Workshop	International Workshop on Semantic Evaluation
Lyhennettä	SemEval
Maa/Alue	Thaimaa
Kaupunki	Bangkok
Ajanjakso	05/08/2021 → 06/08/2021

Pääsy asiakirjaan

10.18653/v1/2021.semeval-1.91Lisenssi: CC BY

2021.semeval-1.91
https://aclanthology.org/faq/
Final published version, 170 KBLisenssi: CC BY

OpenUrl-saatavuus

Koko teksti

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Linkki julkaisuun Scopuksessa

Aka-DigiTala
Kurimo, M., Al-Ghezi, R., Getman, Y. & Voskoboinik, E.
01/09/2019 → 31/08/2023
Projekti: Academy of Finland: Other research funding

Science-IT
Mikko Hakala (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Siteeraa tätä

Voskoboinik, E. (2021). katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English. teoksessa A. Palmer, N. Schneider, N. Schluter, G. Emerson, A. Herbelot, & X. Zhu (Toimittajat), SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop (Sivut 700-705). (SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.semeval-1.91

Voskoboinik, Ekaterina. / katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English. SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop. Toimittaja / Alexis Palmer ; Nathan Schneider ; Natalie Schluter ; Guy Emerson ; Aurelie Herbelot ; Xiaodan Zhu. Association for Computational Linguistics, 2021. Sivut 700-705 (SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop).

@inproceedings{7c01abab219f41e59bfd07b8cef4049b,

title = "katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English",

abstract = "This paper describes systems submitted to SemEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a nonlinear regression models trained to work for both tracks of the task. We show that both systems are able to generalize better when supplied with information about complexities of single word and multi-word expression (MWE) targets simultaneously. This approach proved to be the most beneficial for multi-word expression targets. We also demonstrate that some hand-crafted features differ in their importance for the target types.",

author = "Ekaterina Voskoboinik",

note = "Funding Information: I would like to express my gratitude to the anonymous reviewer for their insightful and helpful comments. I also want thank Ragheb Al-Ghezi for proofreading and Mikko Kurimo for his comments on an earlier draft of the paper. Lastly, I want to say thank you to Science-IT team for providing computational environment used in the experiments. This work is a part of DigiTala project which is funded by the Academy of Finland (grant number 322625). Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; International Workshop on Semantic Evaluation, SemEval ; Conference date: 05-08-2021 Through 06-08-2021",

year = "2021",

doi = "10.18653/v1/2021.semeval-1.91",

language = "English",

series = "SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop",

publisher = "Association for Computational Linguistics",

pages = "700--705",

editor = "Alexis Palmer and Nathan Schneider and Natalie Schluter and Guy Emerson and Aurelie Herbelot and Xiaodan Zhu",

booktitle = "SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop",

address = "United States",

}

Voskoboinik, E 2021, katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English. julkaisussa A Palmer, N Schneider, N Schluter, G Emerson, A Herbelot & X Zhu (toim), SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop. SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop, Association for Computational Linguistics, Sivut 700-705, International Workshop on Semantic Evaluation, Bangkok, Thaimaa, 05/08/2021. https://doi.org/10.18653/v1/2021.semeval-1.91

katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English. / Voskoboinik, Ekaterina.

SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop. toim. / Alexis Palmer; Nathan Schneider; Natalie Schluter; Guy Emerson; Aurelie Herbelot; Xiaodan Zhu. Association for Computational Linguistics, 2021. s. 700-705 (SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

TY - GEN

T1 - katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English

AU - Voskoboinik, Ekaterina

N1 - Funding Information: I would like to express my gratitude to the anonymous reviewer for their insightful and helpful comments. I also want thank Ragheb Al-Ghezi for proofreading and Mikko Kurimo for his comments on an earlier draft of the paper. Lastly, I want to say thank you to Science-IT team for providing computational environment used in the experiments. This work is a part of DigiTala project which is funded by the Academy of Finland (grant number 322625). Publisher Copyright: © 2021 Association for Computational Linguistics.

PY - 2021

Y1 - 2021

N2 - This paper describes systems submitted to SemEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a nonlinear regression models trained to work for both tracks of the task. We show that both systems are able to generalize better when supplied with information about complexities of single word and multi-word expression (MWE) targets simultaneously. This approach proved to be the most beneficial for multi-word expression targets. We also demonstrate that some hand-crafted features differ in their importance for the target types.

AB - This paper describes systems submitted to SemEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a nonlinear regression models trained to work for both tracks of the task. We show that both systems are able to generalize better when supplied with information about complexities of single word and multi-word expression (MWE) targets simultaneously. This approach proved to be the most beneficial for multi-word expression targets. We also demonstrate that some hand-crafted features differ in their importance for the target types.

UR - http://www.scopus.com/inward/record.url?scp=85138925562&partnerID=8YFLogxK

U2 - 10.18653/v1/2021.semeval-1.91

DO - 10.18653/v1/2021.semeval-1.91

M3 - Conference contribution

AN - SCOPUS:85138925562

T3 - SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop

SP - 700

EP - 705

BT - SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop

A2 - Palmer, Alexis

A2 - Schneider, Nathan

A2 - Schluter, Natalie

A2 - Emerson, Guy

A2 - Herbelot, Aurelie

A2 - Zhu, Xiaodan

PB - Association for Computational Linguistics

T2 - International Workshop on Semantic Evaluation

Y2 - 5 August 2021 through 6 August 2021

ER -

Voskoboinik E. katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English. julkaisussa Palmer A, Schneider N, Schluter N, Emerson G, Herbelot A, Zhu X, toimittajat, SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop. Association for Computational Linguistics. 2021. s. 700-705. (SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop). doi: 10.18653/v1/2021.semeval-1.91

katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English

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