Refine
Year of publication
Document Type
- Conference Proceeding (8)
- Article (3)
- Part of a Book (2)
Has Fulltext
- yes (13)
Keywords
- Korpus <Linguistik> (7)
- Englisch (5)
- Automatische Sprachanalyse (3)
- Annotation (2)
- Datenmanagement (2)
- Digital Humanities (2)
- Forschungsdaten (2)
- Sprachdaten (2)
- Sprachvariante (2)
- Sprachwandel (2)
Publicationstate
- Veröffentlichungsversion (8)
- Postprint (2)
- Zweitveröffentlichung (1)
Reviewstate
- Peer-Review (4)
- (Verlags)-Lektorat (3)
- Peer-Revied (1)
- Review-Status-unbekannt (1)
Publisher
This chapter will present lessons learned from CLARIN-D, the German CLARIN national consortium. Members of the CLARIN-D communities and of the CLARIN-D consortium have been engaged in innovative, data-driven, and community-based research, using language resources and tools in the humanities and neigh-bouring disciplines. We will present different use cases and users’ stories that demonstrate the innovative research potential of large digital corpora and lexical resources for the study of language change and variation, for language documentation, for literary studies, and for the social sciences. We will emphasize the added value of making language resources and tools available in the CLARIN distributed research infrastructure and will discuss legal and ethical issues that need to be addressed in the use of such an infrastructure. Innovative technical solutions for accessing digital materials still under copyright and for data mining such materials will be presented. We will outline the need for close interaction with communities of interest in the areas of curriculum development, data management, and training the next generation of digital humanities scholars. The importance of community-supported standards for encoding language resources and the practice of community-based quality control for digital research data will be presented as a crucial step toward the provisioning of high quality research data. The chapter will conclude with a discussion of impor-tant directions for innovative research and for supporting infrastructure development over the next decade and beyond.
We present empirical evidence of the communicative utility of conventionalization, i.e., convergence in linguistic usage over time, and diversification, i.e., linguistic items acquiring different, more specific usages/meanings. From a diachronic perspective, conventionalization plays a crucial role in language change as a condition for innovation and grammaticalization (Bybee, 2010; Schmid, 2015) and diversification is a cornerstone in the formation of sublanguages/registers, i.e., functional linguistic varieties (Halliday, 1988; Harris, 1991). While it is widely acknowledged that change in language use is primarily socio-culturally determined pushing towards greater linguistic expressivity, we here highlight the limiting function of communicative factors on diachronic linguistic variation showing that conventionalization and diversification are associated with a reduction of linguistic variability. To be able to observe effects of linguistic variability reduction, we first need a well-defined notion of choice in context. Linguistically, this implies the paradigmatic axis of linguistic organization, i.e., the sets of linguistic options available in a given or similar syntagmatic contexts. Here, we draw on word embeddings, weakly neural distributional language models that have recently been employed to model lexical-semantic change and allow us to approximate the notion of paradigm by neighbourhood in vector space. Second, we need to capture changes in paradigmatic variability, i.e. reduction/expansion of linguistic options in a given context. As a formal index of paradigmatic variability we use entropy, which measures the contribution of linguistic units (e.g., words) in predicting linguistic choice in bits of information. Using entropy provides us with a link to a communicative interpretation, as it is a well-established measure of communicative efficiency with implications for cognitive processing (Linzen and Jaeger, 2016; Venhuizen et al., 2019); also, entropy is negatively correlated with distance in (word embedding) spaces which in turn shows cognitive reflexes in certain language processing tasks (Mitchel et al., 2008; Auguste et al., 2017). In terms of domain we focus on science, looking at the diachronic development of scientific English from the 17th century to modern time. This provides us with a fairly constrained yet dynamic domain of discourse that has witnessed a powerful systematization throughout the centuries and developed specific linguistic conventions geared towards efficient communication. Overall, our study confirms the assumed trends of conventionalization and diversification shown by diachronically decreasing entropy, interspersed with local, temporary entropy highs pointing to phases of linguistic expansion pertaining primarily to introduction of new technical terminology.
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.
In diesem Panel geht es um die Förderung der geisteswissenschaftlichen Forschung durch eine planvolle Erhebung, Archivierung, Veröffentlichung und die dadurch ermöglichte Nachnutzung von Forschungsdaten, die sowohl zur Qualitätssicherung in der Forschung beitragen als auch nicht zuletzt neue Fragestellungen erlauben. Aus unterschiedlichen Perspektiven soll in dem Panel beleuchtet werden, welchen Mehrwert das Datenmanagement für die Forschung in den digitalen Geisteswissenschaften hat, wie man diesen Mehrwert erreicht und auch die Veröffentlichung der Forschungsdaten als ein selbstverständliches Element der Dissemination der Forschungsergebnisse etabliert und wie man gleichzeitig den Aufwand für die Forschung abschätzen kann.
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.
Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers
(2014)
We present a methodology to analyze the linguistic evolution of scientific registers with data mining techniques, comparing the insights gained from shallow vs. linguistic features. The focus is on selected scientific disciplines at the boundaries to computer science (computational linguistics, bioinformatics, digital construction, microelectronics). The data basis is the English Scientific Text Corpus (SCITEX) which covers a time range of roughly thirty years (1970/80s to early 2000s) (Degaetano-Ortlieb et al., 2013; Teich and Fankhauser, 2010). In particular, we investigate the diversification of scientific registers over time. Our theoretical basis is Systemic Functional Linguistics (SFL) and its specific incarnation of register theory (Halliday and Hasan, 1985). In terms of methods, we combine corpus-based methods of feature extraction and data mining techniques.