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        <title>Entity Recognition at First Sight: Improving NER with Eye Movement Information</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Entity Recognition at First Sight: Improving NER with Eye Movement Information
%A Hollenstein, Nora
%A Zhang, Ce
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F hollenstein-zhang-2019-entity
%X 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.
%R 10.18653/v1/N19-1001
%U https://aclanthology.org/N19-1001
%U https://doi.org/10.18653/v1/N19-1001
%P 1-10

Markdown (Informal)

[Entity Recognition at First Sight: Improving NER with Eye Movement Information](https://aclanthology.org/N19-1001) (Hollenstein & Zhang, NAACL 2019)

• Entity Recognition at First Sight: Improving NER with Eye Movement Information (Hollenstein & Zhang, NAACL 2019)

ACL

• Nora Hollenstein and Ce Zhang. 2019. Entity Recognition at First Sight: Improving NER with Eye Movement Information. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1–10, Minneapolis, Minnesota. Association for Computational Linguistics.