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Unsupervised Text Segmentation Predicts Eye Fixations During Reading
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In: Front Artif Intell (2022)
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Abstract:
Words typically form the basis of psycholinguistic and computational linguistic studies about sentence processing. However, recent evidence shows the basic units during reading, i.e., the items in the mental lexicon, are not always words, but could also be sub-word and supra-word units. To recognize these units, human readers require a cognitive mechanism to learn and detect them. In this paper, we assume eye fixations during reading reveal the locations of the cognitive units, and that the cognitive units are analogous with the text units discovered by unsupervised segmentation models. We predict eye fixations by model-segmented units on both English and Dutch text. The results show the model-segmented units predict eye fixations better than word units. This finding suggests that the predictive performance of model-segmented units indicates their plausibility as cognitive units. The Less-is-Better (LiB) model, which finds the units that minimize both long-term and working memory load, offers advantages both in terms of prediction score and efficiency among alternative models. Our results also suggest that modeling the least-effort principle for the management of long-term and working memory can lead to inferring cognitive units. Overall, the study supports the theory that the mental lexicon stores not only words but also smaller and larger units, suggests that fixation locations during reading depend on these units, and shows that unsupervised segmentation models can discover these units.
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Keyword:
Artificial Intelligence
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URL: https://doi.org/10.3389/frai.2022.731615 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905434/
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Less is Better: A cognitively inspired unsupervised model for language segmentation ...
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Discovering the Language of Wine Reviews: A Text Mining Account
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Enhancing access to online education: quality machine translation of MOOC content
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In: Kordoni, Valia, van den Bosch, Antal orcid:0000-0003-2493-656X , Kermanidis, Katia Lida orcid:0000-0002-3270-5078 , Sosoni, Vilelmini orcid:0000-0002-9583-4651 , Cholakov, Kostadin, Hendrickx, Iris, Huck, Matthias and Way, Andy orcid:0000-0001-5736-5930 (2016) Enhancing access to online education: quality machine translation of MOOC content. In: Tenth International Conference on Language Resources and Evaluation (LREC 2016), 23-28 May 2016, Portorož, Slovenia. ISBN 978-2-9517408-9-1 (2016)
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The Love Equation: Computational Modeling of Romantic Relationships in French Classical Drama
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Enabling the Discovery of Digital Cultural Heritage Objects through Wikipedia
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Strategies for reducing and correcting OCR errors
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In: Volk, Martin; Furrer, Lenz; Sennrich, Rico (2011). Strategies for reducing and correcting OCR errors. In: Sporleder, Caroline; van den Bosch, Antal; Zervanou, Kalliopi. Language Technology for Cultural Heritage. Berlin: Springer, 3-22. (2011)
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Supertags as source language context in hierarchical phrase-based SMT
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In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Kumar Naskar, Sudip, van den Bosch, Antal and Way, Andy orcid:0000-0001-5736-5930 (2010) Supertags as source language context in hierarchical phrase-based SMT. In: AMTA 2010 - 9th Conference of the Association for Machine Translation in the Americas, 31 October - 4 November 2010, Denver, CO, USA. (2010)
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Dependency relations as source context in phrase-based SMT
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In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Naskar, Sudip Kumar, van den Bosch, Antal and Way, Andy orcid:0000-0001-5736-5930 (2009) Dependency relations as source context in phrase-based SMT. In: PACLIC 23 - the 23rd Pacific Asia Conference on Language, Information and Computation, 3-5 December 2009, Hong Kong. (2009)
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A memory-based classification approach to marker-based EBMT
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In: van den Bosch, Antal, Stroppa, Nicolas and Way, Andy orcid:0000-0001-5736-5930 (2007) A memory-based classification approach to marker-based EBMT. In: METIS-II Workshop on New Approaches to Machine Translation, 11 January 2007, Leuven, Belgium. (2007)
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