mlsanityData to 7 Oct 2026

Entity Recognition at First Sight: Improving NER with Eye Movement Information

arXiv 1902.10068 ↗First posted 26 Feb 2019cs.CLcited by 26

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Abstract (arXiv)

Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural model for named entity recognition (NER) with gaze embeddings. These corpora were manually annotated with named entity labels. Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time. The gaze-augmented models for NER using token-level and type-level features outperform the baselines. We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Feb 2019.

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Citing papers per year: 2021: 7; 2022: 2; 2023: 9; 2024: 4; 2025: 2; 2026: 2.

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  1. 2021Multilingual Language Models Predict Human Reading Behaviorcited by 29
  2. 2023Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Readingcited by 18
  3. 2023Synthesizing Human Gaze Feedback for Improved NLP Performancecited by 16
  4. 2021Decoding EEG Brain Activity for Multi-Modal Natural Language Processingcited by 13
  5. 2023ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Textscited by 13
  6. 2021CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signalscited by 11
  7. 2021A Survey on Using Gaze Behaviour for Natural Language Processingcited by 11
  8. 2024Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Modelscited by 11
  9. 2023Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understandingcited by 8
  10. 2022The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Textscited by 7
  11. 2023WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Datasetcited by 6
  12. 2024PoTeC: A German Naturalistic Eye-tracking-while-reading Corpuscited by 5
14 more
  1. 2024EMTeC: A Corpus of Eye Movements on Machine-Generated Textscited by 5
  2. 2021Cognition-aware Cognate Detectioncited by 3
  3. 2023Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networkscited by 3
  4. 2025OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responsescited by 3
  5. 2023GazeReader: Detecting Unknown Word Using Webcam for English as a Second Language (ESL) Learnerscited by 2
  6. 2023DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine Readingcited by 2
  7. 2024Teaching Human Behavior Improves Content Understanding Abilities Of LLMscited by 2
  8. 2021Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Networkcited by 1
  9. 2022Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Taskcited by 1
  10. 2025Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Modelscited by 1
  11. 2021Retrieving Event-related Human Brain Dynamics from Natural Sentence Readingcited by 0
  12. 2023Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processingcited by 0
  13. 2026Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Supportcited by 0
  14. 2026Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Readingcited by 0