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This paper reports on the latest developments of the European Reference Corpus EuReCo and the German Reference Corpus in relation to three of the most important CMLC topics: interoperability, collaboration on corpus infrastructure building, and legal issues. Concerning interoperability, we present new ways to access DeReKo via KorAP on the API and on the plugin level. In addition we report about advancements in the EuReCo- and ICC-initiatives with the provision of comparable corpora, and about recent problems with license acquisitions and our solution approaches using an indemnification clause and model licenses that include scientific exploitation.
We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use—both individually and collectively—over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.
In order to differentiate between figurative and literal usage of verb-noun combinations for the shared task on the disambiguation of German Verbal Idioms issued for KONVENS 2021, we apply and extend an approach originally developed for detecting idioms in a dataset consisting of random ngram samples. The classification is done by implementing a rather shallow, statistics-based pipeline without intensive preprocessing and examinations on the morphosyntactic and semantic level. We describe the overall approach, the differences between the original dataset and the dataset of the KONVENS task, provide experimental classification results, and analyse the individual contributions of our feature sets.
Newspapers became extremely popular in Germany during the 18th and 19th century, and thus increasingly influential for modern German. However, due to the lack of digitized historical newspaper corpora for German, this influence could not be analyzed systematically. In this paper, we introduce the Mannheim Corpus of Digital Newspapers and Magazines, which in its current release comprises 21 newspapers and magazines from the 18th and 19th century. With over 4.1 Mio tokens in about 650 volumes it currently constitutes the largest historical corpus dedicated to newspapers in German. We briefly discuss the prospect of the corpus for analyzing the evolution of news as a genre in its own right and the influence of contextual parameters such as region and register on the language of news. We then focus on one historically influential aspect of newspapers – their role in disseminating foreign words in German. Our preliminary quantitative results indeed indicate that newspapers use foreign words significantly more frequently than other genres, in particular belles lettres.
We present an approach for automatic detection and correction of OCR-induced misspellings in historical texts. The main objective is the post-correction of the digitized Royal Society Corpus, a set of historical documents from 1665 to 1869. Due to the aged material the OCR procedure has made mistakes, thus leading to files corrupted by thousands of misspellings. This motivates a post processing step. The current correction technique is a pattern-based approach which due to its lack of generalization suffers from bad recall.
To generalize from the patterns we propose to use the noisy channel model. From the pattern based substitutions we train a corpus specific error model complemented with a language model. With an F1-Score of 0.61 the presented technique significantly outperforms the pattern based approach which has an F1-score of 0.28. Due to its more accurate error model it also outperforms other implementations of the noisy channel model.
Linguistic Variation and Change in 250 Years of English Scientific Writing: A Data-Driven Approach
(2020)
We trace the evolution of Scientific English through the Late Modern period to modern time on the basis of a comprehensive corpus composed of the Transactions and Proceedings of the Royal Society of London, the first and longest-running English scientific journal established in 1665. Specifically, we explore the linguistic imprints of specialization and diversification in the science domain which accumulate in the formation of “scientific language” and field-specific sublanguages/registers (chemistry, biology etc.). We pursue an exploratory, data-driven approach using state-of-the-art computational language models and combine them with selected information-theoretic measures (entropy, relative entropy) for comparing models along relevant dimensions of variation (time, register). Focusing on selected linguistic variables (lexis, grammar), we show how we deploy computational language models for capturing linguistic variation and change and discuss benefits and limitations.