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Cognitive Processing of Multiword Expressions in Native and Non-native Speakers of English: Evidence from Gaze Data

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Computational and Corpus-Based Phraseology (EUROPHRAS 2017)

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Abstract

Gaze data has been used to investigate the cognitive processing of certain types of formulaic language such as idioms and binominal phrases, however, very little is known about the online cognitive processing of multiword expressions. In this paper we use gaze features to compare the processing of verb - particle and verb - noun multiword expressions to control phrases of the same part-of-speech pattern. We also compare the gaze data for certain components of these expressions and the control phrases in order to find out whether these components are processed differently from the whole units. We provide results for both native and non-native speakers of English and we analyse the importance of the various gaze features for the purpose of this study. We discuss our findings in light of the E-Z model of reading.

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Notes

  1. 1.

    The GECO corpus can be downloaded freely at: http://expsy.ugent.be/downloads/geco.

  2. 2.

    https://typo.uni-konstanz.de/PARSEME/images/shared-task/guidelines/PARSEME-ST-annotation-guidelines-v6.pdf.

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Correspondence to Victoria Yaneva .

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A    Appendix: Distribution of Gaze Features

A    Appendix: Distribution of Gaze Features

Tables 5, 6, 7 and 8 present the distribution of each gaze feature for the V + N and V + P MWEs and Control phrases for both native (L1) and non-native (L2) speakers.

Table 5. Eye-tracking data statistics for V + N MWE
Table 6. Eye-tracking data statistics for V + P MWE
Table 7. Eye-tracking data statistics for V + N Control Phrases
Table 8. Eye-tracking data statistics for V + P Control Phrases

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Yaneva, V., Taslimipoor, S., Rohanian, O., Ha, L.A. (2017). Cognitive Processing of Multiword Expressions in Native and Non-native Speakers of English: Evidence from Gaze Data. In: Mitkov, R. (eds) Computational and Corpus-Based Phraseology. EUROPHRAS 2017. Lecture Notes in Computer Science(), vol 10596. Springer, Cham. https://doi.org/10.1007/978-3-319-69805-2_26

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  • DOI: https://doi.org/10.1007/978-3-319-69805-2_26

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