natural-language-processing-11

Lexical Complexity Prediction

3260 papers • 126 benchmarks • 313 datasets

Predicting the complexity of a word/multi-word expression in a sentence.

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Most implemented papers

cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora

Abhilash Nandy, Sayantan Adak, Tanurima Halder, Sai Mahesh Pokala•Thu Jun 03 2021

This paper fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc.

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Japanese Lexical Complexity for Non-Native Readers: A New Dataset

Masato Mita, Adam Nohejl, Taro Watanabe, Yusuke Ide, Hiroki Ouchi•Thu Jun 29 2023

To study lexical complexity in Japanese, the first Japanese LCP dataset is constructed and the effectiveness of a BERT-based system forJapanese LCP is demonstrated.

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BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models

Soroush Vosoughi, Weicheng Ma, A. Islam•Sun Apr 18 2021

This paper assembles a feature engineering-based model with a deep neural network model founded on BERT to predict the lexical complexity of English words in a given context and demonstrates how they can be harnessed to perform well on the multi word expression subtask.

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ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources

A. Rotaru•Thu Dec 31 2020

This paper describes the participation in the Lexical Complexity Prediction (LCP) shared task of SemEval 2021, which involved predicting subjective ratings of complexity for English single words and multi-word expressions, presented in context.

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Automatic Readability Assessment of German Sentences with Transformer Ensembles

Patrick Gustav Blaneck, Tobias Bornheim, Niklas Grieger, S. Bialonski•Thu Sep 08 2022

The ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences is studied and the dependence of prediction performance on ensemble size and composition is investigated.

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