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One Size Does Not Fit All: The Case for Personalised Word Complexity Models

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Sian Gooding, Manuel Tragut

Computation and Language · Submitted 5 May 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this paper, we show that personal models are best when predicting word complexity for individual readers.

how it is stated · asserted 1.00 read the record →

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arXiv ID2205.02564 (version 1)
Pith IDJF2YKHSS, Pith's permanent record of this paper and its review
CitationManuel Tragut, Sian Gooding. "One Size Does Not Fit All: The Case for Personalised Word Complexity Models." arXiv:2205.02564 (2022). https://pith.science/paper/JF2YKHSS
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,180 input tokens.

What the abstract itself states, as probabilities: claims first 0.12 · states limitation 0.14 · human participants 0.77 · controlled comparison 0.25 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.39 · mentions code or data availability 0.85.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience. In this paper, we show that personal models are best when predicting word complexity for individual readers. We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. background 1.00 0.05 0.02 asserted 0.88
s1 It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. background 1.00 0.06 0.02 asserted 1.00
s2 However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience. background 0.61 0.08 0.04 asserted 1.00
s3 In this paper, we show that personal models are best when predicting word complexity for individual readers. result 0.98 0.94 0.94 asserted 1.00
s4 We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research. contribution 0.89 0.96 0.08 asserted 0.96

What kind of paper the classifier reports this is: dataset or benchmark 0.59 · evidence from observation or experiment 0.96.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience. In this paper, we show that personal models are best when predicting word complexity for individual readers. We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 3 sentences without a confident label

Cites as background 1 paper

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