Mapping the Perfect via Translation Mining

Klis, Martijn van der; Bruyn, Bert Le; Swart, H.E. de

doi:10.18653/v1/e17-2080

Cited by 7 publications

(16 citation statements)

References 7 publications

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“…[4] The software applications PerfectExtractor and TimeAlign have been developed in collaboration with the Utrecht Digital Humanities Lab, and are freely available through the project's website, see https://time-in-translation.hum.uu.nl/. The technicalities of the Translation Mining methodology are outlined in Van der Klis et al (2017), and illustrated there with a multilingual dataset of PERFECTs extracted from the Europarl parallel corpus (Koehn 2005). Here, we focus on the use we make of the Translation Mining methodology for linguistic analysis.…”

Section: Data Collection and Annotationmentioning

confidence: 99%

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A multilingual corpus study of the competition betweenpastandperfectin narrative discourse

Klis¹,

Bruyn²,

Swart³

2021

J. Ling.

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The western European present perfect is subject to substantial crosslinguistic variation. The literature, however, focuses on individual languages or on comparisons of a restricted number of languages. We piece together the puzzle and do so in a data-driven way by comparing the use of the present perfect through a parallel corpus based on the French novel L’Étranger and its translations in Italian, German, Dutch, European Spanish, British English, and Modern Greek. We introduce and showcase Translation Mining, a software suite combining a parallel corpus database with annotation and analysis tools. Translation Mining allows us to generate descriptive statistics of tense use across languages but also to visualize variation through its multidimensional scaling component and to link the variation we find to the underlying data through its integrated setup. We confirm that the present perfect competes with the past and we reveal the fine-grained scalar nature of the variation. To complete the puzzle, we ascertain the dimensions of variation, ranging from lexical and compositional semantics to dynamic semantics and pragmatics.1

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Section: Data Collection and Annotationmentioning

confidence: 99%

“…As one could expect with 29 attested 7-tuple types, clusters are not always easy to [7] We leave aside technicalities on MDS here. See Van der Klis et al (2017) and Van der Klis & Tellings (published online 13 March 2021) for a more thorough introduction to MDS and the way we implement it here.…”

Section: A Cartographic Inventory Of the Datamentioning

confidence: 99%

A multilingual corpus study of the competition betweenpastandperfectin narrative discourse

Klis¹,

Bruyn²,

Swart³

2021

J. Ling.

Self Cite

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“…The methodology relies on parallel corpus data, and we use multidimensional scaling as a statistical and visualization technique to reveal the patterns. This resembles the approaches in Wälchli and Cysouw (2012), Wälchli (2018Wälchli ( /2019, and has been dubbed Translation Mining by van der Klis et al (2017). The methodology will be introduced in Section 3, but see van der Klis and Tellings (2022) for a more exhaustive overview.…”

Section: Before (Na)mentioning

confidence: 99%

“…MDS is a statistical technique that reduces a complex dataset with variation in many dimensions to a lower-dimensional representation that can be displayed visually as a scatterplot, known as a semantic map. This methodology has been used both in large-scale cross-linguistic examinations, such as Croft and Poole (2008) and Wälchli and Cysouw (2012), as well as in studies comparing just a few languages, such as van der Klis et al (2017). van der Klis and Tellings (2022) provide the technical background of MDS, an explanation of how to interpret MDS maps, and an overview of the application of MDS in linguistic theory.…”

Section: Mds Semantic Mapsmentioning

confidence: 99%

Not…Until across European Languages: A Parallel Corpus Study

2022

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We present a parallel corpus study on the expression of the temporal construction ‘not…until’ in a sample of European languages. We use data from the Europarl corpus and create semantic maps by multidimensional scaling, in order to analyze cross-linguistic and language-internal variation. This paper builds on formal semantic and typological work, extending it by including conditional constructions, as well as connectives of the type as long as. In an investigation of 7 languages, we find that (i) languages use many more different constructions to convey this meaning than was expected from the literature; and (ii) the combination of polarity marking (negation/assertion) strongly correlates with the type of connective. We corroborate our results in a larger sample of 21 European languages. An analysis of clusters and dimensions of the semantic maps based on the enlarged dataset shows that connectives are not randomly distributed across the semantic space of the ‘not…until’-domain.

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“…In the GMB and the AMR Bank tense is simply ignored. Annotating aspect is complex-for instance, the use of the perfect differs enormously even between closely related languages such as English, Dutch, and Italian (van der Klis et al, 2017). These complications lead to a simple annotation model in the PMB where tense is reduced to a manageable set of three tenses: past, present and future.…”

Section: Divide and Conquermentioning

confidence: 99%