Text Simplification using Word Sense Disambiguation Using Knowledge-Based Approach
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Updated
Aug 14, 2017 - Python
Text Simplification using Word Sense Disambiguation Using Knowledge-Based Approach
Simplify the web and remove unwanted clutter.
A collection of tools for sentence alignement
An implementation of transformer-based language model for sentence rewriting tasks such as summarization, simplification, and grammatical error correction.
This is the reimplementation of the NeuralTextSimplification system in Pytorch.
NovoLS is a lexical text simplification system which I constructed as part of my dissertation project. It is largely insipired by LightLS — an older lexcial text simplifier proposed by Prof. Dr. Goran Glavaš and Dr. Sanja Štajner in 2015.
MILES is a multilingual text simplifier inspired by LSBert - A BERT-based lexical simplification approach proposed in 2018. Unlike LSBert, MILES uses the bert-base-multilingual-uncased model, as well as simple language-agnostic approaches to complex word identification (CWI) and candidate ranking.
Extract text from Vikidia/Wikipedia articles [fr]
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders
Dataset containing scroll interactions of 598 partcipants reading advanced and elementary texts from the OneStopEnglish corpus
simplify is a text simplification toolkit
An unsupervised approach to sentence simplification that combines text generation and text revision.
Code and data for discourse-based sentence splitting experiments.
Meta-evaluation of automatic metrics in Text Simplification
the code and resources for multi-level complexity-controllable MT
Dataset with manual simplifications of COVID-19 information in English and Spanish.
Klexikon: A German Dataset for Joint Summarization and Simplification
Dataset for automatic readability assessment and text simplification of sentences in Italian administrative language.
Unsupervised Sentence Simplification via Dependency Parsing
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