@inproceedings{rozi-etal-2021-stanford,
title = "{S}tanford {ML}ab at {S}em{E}val-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings",
author = "Rozi, Erik and
Iyer, Niveditha and
Chi, Gordon and
Choe, Enok and
Lee, Kathy J. and
Liu, Kevin and
Liu, Patrick and
Lack, Zander and
Tang, Jillian and
Chi, Ethan A.",
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-1.89",
doi = "10.18653/v1/2021.semeval-1.89",
pages = "688--693",
abstract = "This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction. Text comprehension depends on the reader{'}s ability to understand the words present in it; evaluating the lexical complexity of such texts can enable readers to find an appropriate text and systems to tailor a text to an audience{'}s needs. We present our model pipeline, which applies a combination of embedding-based and manual features to predict lexical complexity on the CompLex English dataset using various tree-based and linear models. Our method is ranked 27 / 54 on single-word prediction and 14 / 37 on multi-word prediction.",
}
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<abstract>This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction. Text comprehension depends on the reader’s ability to understand the words present in it; evaluating the lexical complexity of such texts can enable readers to find an appropriate text and systems to tailor a text to an audience’s needs. We present our model pipeline, which applies a combination of embedding-based and manual features to predict lexical complexity on the CompLex English dataset using various tree-based and linear models. Our method is ranked 27 / 54 on single-word prediction and 14 / 37 on multi-word prediction.</abstract>
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%0 Conference Proceedings
%T Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings
%A Rozi, Erik
%A Iyer, Niveditha
%A Chi, Gordon
%A Choe, Enok
%A Lee, Kathy J.
%A Liu, Kevin
%A Liu, Patrick
%A Lack, Zander
%A Tang, Jillian
%A Chi, Ethan A.
%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 rozi-etal-2021-stanford
%X This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction. Text comprehension depends on the reader’s ability to understand the words present in it; evaluating the lexical complexity of such texts can enable readers to find an appropriate text and systems to tailor a text to an audience’s needs. We present our model pipeline, which applies a combination of embedding-based and manual features to predict lexical complexity on the CompLex English dataset using various tree-based and linear models. Our method is ranked 27 / 54 on single-word prediction and 14 / 37 on multi-word prediction.
%R 10.18653/v1/2021.semeval-1.89
%U https://aclanthology.org/2021.semeval-1.89
%U https://doi.org/10.18653/v1/2021.semeval-1.89
%P 688-693
Markdown (Informal)
[Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings](https://aclanthology.org/2021.semeval-1.89) (Rozi et al., SemEval 2021)
ACL
- Erik Rozi, Niveditha Iyer, Gordon Chi, Enok Choe, Kathy J. Lee, Kevin Liu, Patrick Liu, Zander Lack, Jillian Tang, and Ethan A. Chi. 2021. Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 688–693, Online. Association for Computational Linguistics.