2022
DOI: 10.3390/languages7030158
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The Discovery of Aspect: A Heuristic Parallel Corpus Study of Ingressive, Continuative and Resumptive Viewpoint Aspect

Abstract: Languages differ in how systematically and obligatorily they encode conceptual categories such as tense and aspect. By drawing on large parallel corpora, these differences can be exploited heuristically: expressive obligatoriness and the systematicity of a conceptual category in one language can function as a probe for other languages that do not (evidently) encode it. This study applies this method—called heuristic translation mining (HTM)—to viewpoint aspect in Mandarin (an aspect-oriented language) and Dutc… Show more

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Cited by 6 publications
(18 citation statements)
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References 54 publications
(26 reference statements)
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“…The rationale behind our selection of parallel corpus traditions is that they represent the three strategies that are a priori available to control for translation biases within corpus research: the Typological tradition restricts the scope of the questions that can be asked, and the Contrastive and Translation Mining traditions rely on monolingual and parallel control corpora, respectively. We are confident that other traditions, such as Multiple Parallel Text Analysis (Lu and Verhagen 2016;Lu et al 2018), Heuristic Translation Mining (Bogaards 2019(Bogaards , 2022 etc., can insightfully be related to one of the traditions discussed here. 1…”
Section: Parallel Corpus Traditionsmentioning
confidence: 92%
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“…The rationale behind our selection of parallel corpus traditions is that they represent the three strategies that are a priori available to control for translation biases within corpus research: the Typological tradition restricts the scope of the questions that can be asked, and the Contrastive and Translation Mining traditions rely on monolingual and parallel control corpora, respectively. We are confident that other traditions, such as Multiple Parallel Text Analysis (Lu and Verhagen 2016;Lu et al 2018), Heuristic Translation Mining (Bogaards 2019(Bogaards , 2022 etc., can insightfully be related to one of the traditions discussed here. 1…”
Section: Parallel Corpus Traditionsmentioning
confidence: 92%
“…The assumption of representativeness is, in general, not taken to be absolute: most papers that use parallel corpus data acknowledge that translated language differs from untranslated language and stress the preliminary or qualitative nature of their observations (see, e.g., Bogaards (2022), Grønn and von Stechow (2020)). At the same time, we find that recent studies using parallel corpora neatly succeed in replicating and fine-tuning crosslinguistic patterns that are predicted by the literature (see, e.g., Beekhuizen et al (2017) on indefinite pronouns, and van der Klis et al (2021a) on the HAVE-PERFECT), suggesting that the conclusions we can draw from parallel data are more reliable than what is generally assumed.…”
Section: Introductionmentioning
confidence: 99%
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“…We use a new methodology in which parallel corpora are analyzed to compare crosslinguistic data (e.g., Wälchli and Cysouw 2012;van der Klis et al 2017van der Klis et al , 2021Bogaards 2022;Mulder et al 2022). We gather our data from three different Spanish translations of chapters 1, 16 and 17 of J.K. Rowling's Harry Potter and the Philosopher's Stone.…”
Section: Methodology: Parallel Corpus and Multidimensional Scalingmentioning
confidence: 99%
“…I should point out, however, that there is some notable work that goes beyond examining aan het VINF zijn as an isolated progressive construction, which the present study takes as a starting point. For example, Bogaards et al (2022) compare the aan het-progressive to the ingressive variants with gaan 'go' or slaan 'hit' as the matrix verb, as in 2a. They conclude that these are two different syntactic constructions and situate the aan hetprogressive higher on a synchronic lexical-to-functional cline than the aan het-ingressive (that is, the former is more grammaticalized).…”
Section: The Aan Het-progressivementioning
confidence: 99%