@inproceedings{liebeskind-etal-2021-jct,
title = "{JCT} at {S}em{E}val-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction",
author = "Liebeskind, Chaya and
Elkayam, Otniel and
Liebeskind, Shmuel",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.13",
doi = "10.18653/v1/2021.semeval-1.13",
pages = "138--143",
abstract = "In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="liebeskind-etal-2021-jct">
<titleInfo>
<title>JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chaya</namePart>
<namePart type="family">Liebeskind</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Otniel</namePart>
<namePart type="family">Elkayam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shmuel</namePart>
<namePart type="family">Liebeskind</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natalie</namePart>
<namePart type="family">Schluter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Guy</namePart>
<namePart type="family">Emerson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aurelie</namePart>
<namePart type="family">Herbelot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaodan</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words</abstract>
<identifier type="citekey">liebeskind-etal-2021-jct</identifier>
<identifier type="doi">10.18653/v1/2021.semeval-1.13</identifier>
<location>
<url>https://aclanthology.org/2021.semeval-1.13</url>
</location>
<part>
<date>2021-08</date>
<extent unit="page">
<start>138</start>
<end>143</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction
%A Liebeskind, Chaya
%A Elkayam, Otniel
%A Liebeskind, Shmuel
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F liebeskind-etal-2021-jct
%X In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words
%R 10.18653/v1/2021.semeval-1.13
%U https://aclanthology.org/2021.semeval-1.13
%U https://doi.org/10.18653/v1/2021.semeval-1.13
%P 138-143
Markdown (Informal)
[JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction](https://aclanthology.org/2021.semeval-1.13) (Liebeskind et al., SemEval 2021)
ACL