Optimizing scientific communication : the role of relative clauses as markers of complexity in English and German scientific writing between 1650 and 1900

The aim of this thesis is to show that both scientific English and German have become increasingly optimized for scientific communication from 1650 to 1900 by adapting the usage of relative clauses as markers of grammatical complexity. While the lexico-grammatical changes in terms of features and their frequency distribution in scientific writing during this period are well documented, in the present work we are interested in the underlying factors driving these changes and how they affect efficient scientific communication. As the scientific register emerges and evolves, it continuously adapts to the changing communicative needs posed by extra-linguistic pressures arising from the scientific community and its achievements. We assume that, over time, scientific language maintains communicative efficiency by balancing lexico-semantic expansion with a reduction in (lexico-)grammatical complexity on different linguistic levels. This is based on the idea that linguistic complexity affects processing difficulty and, in turn, communicative efficiency. To achieve optimization, complexity is adjusted on the level of lexico-grammar, which is related to expectation-based processing cost, and syntax, which is linked to working memory-based processing cost. We conduct five corpus-based studies comparing English and German scientific writing to general language. The first two investigate the development of relative clauses in terms of lexico-grammar, measuring the paradigmatic richness and syntagmatic predictability of relativizers as indicators of expectation-based processing cost. The results confirm that both levels undergo a reduction in complexity over time. The other three studies focus on the syntactic complexity of relative clauses, investigating syntactic intricacy, locality, and accessibility. Results show that intricacy and locality decrease, leading to lower grammatical complexity and thus mitigating memory-based processing cost. However, accessibility is not a factor of complexity reduction over time. Our studies reveal a register-specific diachronic complexity reduction in scientific language both in lexico-grammar and syntax. The cross-linguistic comparison shows that English is more advanced in its register-specific development while German lags behind due to a later establishment of the vernacular as a language of scientific communication.This work is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 232722074 – SFB 110

Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German

We present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.--19th c.) annotated with Universal Dependencies. We describe several steps of data pre-processing and evaluate the resulting parsing accuracy showing how our pre-processing steps significantly improve output quality. As a sanity check for the representativity of our data, we conduct a case study comparing previously gained insights on grammatical change in the scientific genre with our data. Our results reflect the often reported trend of English scientific discourse towards heavy noun phrases and a simplification of the sentence structure (Halliday, 1988; Halliday and Martin, 1993; Biber and Gray, 2011; Biber and Gray, 2016). We also show that this trend applies to German scientific discourse as well. The presented corpora are valuable resources suitable for the contrastive analysis of syntactic diachronic change in the scientific genre between 1650 and 1900. The presented pre-processing procedures and their evaluations are applicable to other languages and can be useful for a variety of Natural Language Processing tasks such as syntactic parsing.This work is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 232722074 – SFB 1102

Relativizers as markers of grammatical complexity: A diachronic, cross-register study of English and German

In this paper, we investigate grammatical complexity as a register feature of scientific English and German. Specifically, we carry out a diachronic comparison between general and scientific discourse in the two languages from the 17th to the 19th century, using relativizers as proxies for grammatical complexity. We ground our study in register theory (Halliday and Hasan, 1985), assuming that language use reflects contextual factors, which contribute to the formation of registers (Quirk et al., 1985; Biber et al., 1999; Teich et al., 2016). Our findings show a clear tendency towards grammatical simplification in scientific discourse in both languages with English spearheading the trend early on and German following later

Coreference Strategies in English-German Translation

We present a study focusing on variation of coreferential devices in English original TED talks and news texts and their German translations. Using exploratory techniques we contemplate a diverse set of coreference devices as features which we assume indicate language-specific and register-based variation as well as potential translation strategies. Our findings reflect differences on both dimensions with stronger variation along the lines of register than between languages. By exposing interactions between text type and cross-linguistic variation, they can also inform multilingual NLP applications, especially machine translation

Coreference Strategies in English-German Translation

We present a study focusing on variation of coreferential devices in English original TED talks and news texts and their German translations. Using exploratory techniques we contemplate a diverse set of coreference devices as features which we assume indicate language-specific and register-based variation as well as potential translation strategies. Our findings reflect differences on both dimensions with stronger variation along the lines of register than between languages. By exposing interactions between text type and cross-linguistic variation, they can also inform multilingual NLP applications, especially machine translation

Coreference in Universal Dependencies 1.0 (CorefUD 1.0)

CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 1.0 consists of 17 datasets for 11 languages. The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column. The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 13 datasets for 10 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 1 for Hungarian, 1 for Lithuanian, 1 for Polish, 1 for Russian, and 1 for Spanish), excluding the test data. The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets. When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource too. Version 1.0 consists of the same corpora and languages as the previous version 0.2; however, the English GUM dataset has been updated to a newer and larger version, and in the Czech/English PCEDT dataset, the train-dev-test split has been changed to be compatible with OntoNotes. Nevertheless, the main change is in the file format (the MISC attributes have new form and interpretation)

CorefUD 1.0

CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 0.2 consists of 17 datasets for 11 languages, and compared to the version 0.2, the file format has been reworked and a number of annotation errors have been fixed

Wohin damit? Storing and reusing my language data: Minute Madness der Datenzentren

Präsentiert beim Workshop "Wohin damit? Storing and reusing my language data" am 22. Juni 2023 in Mannheim. Die Präsentation wurde im Kontext der Arbeit des Vereins Nationale Forschungsdateninfrastruktur (NFDI) e.V. gehalten

Coreference in Universal Dependencies 1.1 (CorefUD 1.1)

CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 1.1 consists of 21 datasets for 13 languages. The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column. The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 17 datasets for 12 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 2 for Hungarian, 1 for Lithuanian, 2 for Norwegian, 1 for Polish, 1 for Russian, 1 for Spanish, and 1 for Turkish), excluding the test data. The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets. When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource too. Compared to the previous version 1.0, the version 1.1 comprises new languages and corpora, namely Hungarian-KorKor, Norwegian-BokmaalNARC, Norwegian-NynorskNARC, and Turkish-ITCC. In addition, the English GUM dataset has been updated to a newer and larger version, and the conversion pipelines for most datasets have been refined (a list of all changes in each dataset can be found in the corresponding README file)