# Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset

arXiv: https://arxiv.org/abs/2102.08773 · HTML: https://mlsanity.com/paper/2102.08773/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 95 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Linguistic Features for Readability Assessment](https://mlsanity.com/paper/2006.00377/) (2020)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification](https://mlsanity.com/paper/1810.05754/) (2018)
- [CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data](https://mlsanity.com/paper/2003.07008/) (2020)
- [Strong Baselines for Complex Word Identification across Multiple Languages](https://mlsanity.com/paper/1904.05953/) (2019)
- [Complex Word Identification: Challenges in Data Annotation and System Performance](https://mlsanity.com/paper/1710.04989/) (2017)
- [UnibucKernel: A kernel-based learning method for complex word identification](https://mlsanity.com/paper/1803.07602/) (2018)
- [CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification](https://mlsanity.com/paper/1709.02843/) (2017)

## Built on by (most cited first, 15 of 15)

- [Language Variety Identification with True Labels](https://mlsanity.com/paper/2303.01490/) (2023)
- [UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction](https://mlsanity.com/paper/2104.06983/) (2021)
- [Japanese Lexical Complexity for Non-Native Readers: A New Dataset](https://mlsanity.com/paper/2306.17399/) (2023)
- [LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction](https://mlsanity.com/paper/2105.08780/) (2021)
- [Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification](https://mlsanity.com/paper/2205.07283/) (2022)
- [Lexical Complexity Prediction: An Overview](https://mlsanity.com/paper/2303.04851/) (2023)
- [LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures](https://mlsanity.com/paper/2105.09653/) (2021)
- [RALS: Resources and Baselines for Romanian Automatic Lexical Simplification](https://mlsanity.com/paper/2607.20078/) (2026)
- [OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction](https://mlsanity.com/paper/2105.05535/) (2021)
- [MultiLS: A Multi-task Lexical Simplification Framework](https://mlsanity.com/paper/2402.14972/) (2024)
- [Using Letter Positional Probabilities to Assess Word Complexity](https://mlsanity.com/paper/2404.07768/) (2024)
- [CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning](https://mlsanity.com/paper/2510.18466/) (2025)
- [Marginal Alignment Does Not Guarantee Joint-Distribution Fidelity: An Official-Reference Audit of Nemotron-Personas-Korea with Cross-Locale Replication](https://mlsanity.com/paper/2606.12433/) (2026)
- [UOL@IDEM at BEA 2026 Shared Task 1: Neural Fusion and Feature-Rich Modeling for L1-Aware Vocabulary Difficulty Prediction](https://mlsanity.com/paper/2606.24501/) (2026)
- [SimpCue: Cue-Based Prompting for Multilingual Text Simplification](https://mlsanity.com/paper/2608.28042/) (2026)

## Abstract (arXiv)

Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for assessing the readability of a text. This task is commonly referred to as Complex Word Identification (CWI) and is often modelled as a supervised classification problem. For training such systems, annotated datasets in which words and sometimes multi-word expressions are labelled regarding complexity are required. In this paper we analyze previous work carried out in this task and investigate the properties of CWI datasets for English. We develop a protocol for the annotation of lexical complexity and use this to annotate a new dataset, CompLex 2.0. We present experiments using both new and old datasets to investigate the nature of lexical complexity. We found that a Likert-scale annotation protocol provides an objective setting that is superior for identifying the complexity of words compared to a binary annotation protocol. We release a new dataset using our new protocol to promote the task of Lexical Complexity Prediction.