# A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification

arXiv: https://arxiv.org/abs/1810.05754 · HTML: https://mlsanity.com/paper/1810.05754/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 24 of 24)

- [SemEval-2021 Task 1: Lexical Complexity Prediction](https://mlsanity.com/paper/2106.00473/) (2021)
- [Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset](https://mlsanity.com/paper/2102.08773/) (2021)
- [One Size Does Not Fit All: The Case for Personalised Word Complexity Models](https://mlsanity.com/paper/2205.02564/) (2022)
- [Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification](https://mlsanity.com/paper/2305.12463/) (2023)
- [The SAMER Arabic Text Simplification Corpus](https://mlsanity.com/paper/2404.18615/) (2024)
- [A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss](https://mlsanity.com/paper/2210.10305/) (2022)
- [Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency](https://mlsanity.com/paper/2111.15156/) (2021)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Exploiting Summarization Data to Help Text Simplification](https://mlsanity.com/paper/2302.07124/) (2023)
- [Deep Learning Approaches to Lexical Simplification: A Survey](https://mlsanity.com/paper/2305.12000/) (2023)
- [Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding](https://mlsanity.com/paper/2305.14592/) (2023)
- [LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction](https://mlsanity.com/paper/2105.08780/) (2021)
- [Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment](https://mlsanity.com/paper/2107.04268/) (2021)
- [Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification](https://mlsanity.com/paper/2205.07283/) (2022)
- [Unsupervised Simplification of Legal Texts](https://mlsanity.com/paper/2209.00557/) (2022)
- [Lexical Complexity Prediction: An Overview](https://mlsanity.com/paper/2303.04851/) (2023)
- [SimpLex: a lexical text simplification architecture](https://mlsanity.com/paper/2304.07002/) (2023)
- [SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words](https://mlsanity.com/paper/2204.07779/) (2022)
- [Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring](https://mlsanity.com/paper/2211.13373/) (2022)
- [Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification](https://mlsanity.com/paper/2404.01029/) (2024)
- [Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese](https://mlsanity.com/paper/2406.02974/) (2024)
- [Understanding the Cognitive Complexity in Language Elicited by Product Images](https://mlsanity.com/paper/2409.16521/) (2024)
- [Readability Measures and Automatic Text Simplification: In the Search of a Construct](https://mlsanity.com/paper/2511.09536/) (2025)
- [ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translation](https://mlsanity.com/paper/2606.05421/) (2026)

## Abstract (arXiv)

Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).