Abstract
When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones. In this paper, we use eye-tracking data to learn how humans perform this disambiguation. We use this knowledge to improve the automatic classification of it. We show that by using gaze data and a POS-tagger we are able to significantly outperform a common baseline and classify between three categories of it with an accuracy comparable to that of linguisticbased approaches. In addition, the discriminatory power of specific gaze features informs the way humans process the pronoun, which, to the best of our knowledge, has not been explored using data from a natural reading task.Citation
Yaneva, V., Ha, L.A., Evans, R. and Mitkov, R. (2018) Classifying Referential and Non-referential It Using Gaze. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4896–4901, Brussels, Belgium. Association for Computational Linguistics.Journal
Association for Computational Linguistics (ACL)Additional Links
https://www.aclweb.org/anthology/D18-1528/Type
Conference contributionLanguage
enISBN
9781948087841ae974a485f413a2113503eed53cd6c53
10.18653/v1/D18-1528
Scopus Count
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- Creative Commons
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