Recursive Context-Aware Lexical Simplification

Gooding, Sian; Kochmar, Ekaterina

doi:10.18653/v1/d19-1491

Cited by 18 publications

(18 citation statements)

References 25 publications

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“…For instance, in example (1) a CWI system might identify engulfed as a complex word, which would allow an LS system to replace it with a simpler alternative, e.g. flooded, in the next step (Paetzold and Specia, 2016a;Gooding and Kochmar, 2019b):…”

Section: Introductionmentioning

confidence: 99%

Word Complexity is in the Eye of the Beholder

Gooding

Kochmar²,

Yimam

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Lexical complexity is a highly subjective notion, yet this factor is often neglected in lexical simplification and readability systems which use a "one-size-fits-all" approach. In this paper, we investigate which aspects contribute to the notion of lexical complexity in various groups of readers, focusing on native and nonnative speakers of English, and how the notion of complexity changes depending on the proficiency level of a non-native reader. To facilitate reproducibility of our approach and foster further research into these aspects, we release a dataset of complex words annotated by readers with different backgrounds.

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Section: Introductionmentioning

confidence: 99%

Word Complexity is in the Eye of the Beholder

Gooding

Kochmar²,

Yimam

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…Similar as proposed in Gooding and Kochmar (2019); Hartmann and dos Santos (2018), and De Hertog and Tack (2018) we use word and character embeddings. We compare pretrained non-contextualized word embeddings, i.e., GloVe (Pennington et al, 2014), pre-trained contextualized word embeddings, i.e., ELMo (Peters et al, 2018) and BERT (Devlin et al, 2019), with pre-trained contextualized character embeddings, i.e., stacked Flair (Akbik et al, 2018(Akbik et al, , 2019a) -a combination of GloVe and Flair-and PooledFlair (Akbik et al, 2019b).…”

Section: Word and Character Embeddingsmentioning

confidence: 99%

RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction

Stodden¹,

Venugopal²

2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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We present the technical report of the system called RS GV at SemEval-2021 Task 1 on complexity prediction of English words. RS GV is a neural network using hand-crafted linguistic features in combination with character and word embeddings to predict the target words' complexity. For the generation of the handcrafted features, we set the target words in relation to their senses. RS GV predicts the complexity well of biomedical terms but it has problems with the complexity prediction of very complex and very simple target words.

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“…These scores indicate which words are likely to cause problems for a reader. The words that are identified as problematic can be the subject of numerous types of intervention, such as direct replacement in the setting of lexical simplification (Gooding and Kochmar, 2019), or extra information being given in the context of explanation generation .…”

Section: Introductionmentioning

confidence: 99%

SemEval-2021 Task 1: Lexical Complexity Prediction

Shardlow¹,

Evans²,

Paetzold³

et al. 2021

Preprint

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This paper presents the results and main findings of SemEval-2021 Task 1 -Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al., 2020). CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale. SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs. The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.

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Recursive Context-Aware Lexical Simplification

Cited by 18 publications

References 25 publications

Word Complexity is in the Eye of the Beholder

Word Complexity is in the Eye of the Beholder

RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction

SemEval-2021 Task 1: Lexical Complexity Prediction

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