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Cover Page The handle http://hdl.handle.net/1887/92886 holds various files of this Leiden University dissertation. Author: Lensink, S.E. Title: Processing Lexical Bundles Issue Date: 2020-06-04

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Cover Page

The handle

http://hdl.handle.net/1887/92886

holds various files of this Leiden University

dissertation.

Author:

Lensink, S.E.

(2)

Stellingen

Behorende bij het proefschrift Processing lexical bundles

Saskia E. Lensink

1. So far, there is no evidence that additional language experience has any measurable consequences for how lexical bundles are read, which does not concur with predictions from a usage-based

approach.

2. Even though lexical bundles are processed as wholes, the language system also analyses their internal structure and takes into account their constituent parts in parallel.

3. Measures extracted from a discriminative model of language are better predictors of eye-tracking data than traditional frequency measures, and moreover provide a more complete and nuanced view on how both top-down and bottom-up processes contribute to processing.

4. Lexical access to lexical bundles involves similar stages as lexical access to single words.

5. Linguistic theory should consider the possibility that units of form and meaning are not necessarily single words or opaque idioms, but could also consist of transparent, frequent combinations of words.

6. Carrying out and reporting on replications should become a standard practice in experimental linguistics, and preferably in any experimental field.

7. Linguistics should fully embrace the opportunities offered by machine learning, for it can bring to light new and unexpected patterns, offering us new ideas, insights, and questions.

8. Despite the great opportunities offered by quantitative methods, much of language is

unquantifiable (humor, creativity, pragmatics…). Linguists should never stop trying to study these unquantifiable parts.

9. Any Artificial Intelligence strategy should advice on when to rely on a machine’s intelligence and suggestions, as humans can be biased and blind for the unexpected, and when to rely on human intelligence, insights, and interpretations, as a machine’s output is just as biased as the data we feed it, and its answers fully depend on the questions we ask it.

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