Lexical Complexity Prediction
8 papers with code • 0 benchmarks • 0 datasets
Predicting the complexity of a word/multi-word expression in a sentence.
Benchmarks
These leaderboards are used to track progress in Lexical Complexity Prediction
Most implemented papers
cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora
abhi1nandy2/CS60075-Team-2-Task-1
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This paper describes the performance of the team cs60075_team2 at SemEval 2021 Task 1 - Lexical Complexity Prediction.
Japanese Lexical Complexity for Non-Native Readers: A New Dataset
Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale.
CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data
With a few exceptions, previous studies have approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) for a set of target words in a text.
IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task
This paper describes our contribution to SemEval 2021 Task 1: Lexical Complexity Prediction.
BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models
Aadil101/BigGreen-at-LCP-2021
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SEMEVAL 2021
This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context.
ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources
armandrotaru/teamandi-lcp
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SEMEVAL 2021
In this paper we describe our 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.
cs60075\_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora
abhi1nandy2/CS60075-Team-2-Task-1
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SEMEVAL 2021
The main contribution of this paper is to 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.
Automatic Readability Assessment of German Sentences with Transformer Ensembles
dslaborg/tcc2022
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GermEval 2022
In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences.
