mlsanityData to 7 Oct 2026

LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach

arXiv 2104.13043 ↗First posted 27 Apr 2021cs.CLcited by 5

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Abstract (arXiv)

A LightGBM model fed with target word lexical characteristics and features obtained from word frequency lists, psychometric data and bigram association measures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data Prediction. It obtained the best performance of all teams on two of the five eye-tracking measures to predict, allowing it to rank first on the official challenge criterion and to outperform all deep-learning based systems participating in the challenge.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 2 of its 35 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

2 of 35 references.

References · 2

  1. 2019ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotationcited by 65
  2. 2019CogniVal: A Framework for Cognitive Word Embedding Evaluationcited by 27

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 2.

5 papers.