First, what CLS is about

• Computational Literary Studies
• Aimed at analyzing (large amounts of) textual data…
• … by computational techniques

Foundations of CLS

• Computation into criticism
• Distant reading
• Stylometry
• Authorship attribution
• Digital humanities
• Language resources
• Digital libraries
• Natural language processing
• Machine learning
• Big data
• …

1,000 Polish novels

Combination of factors needed

• Datasets (language resources)
• Tools (computer programs)
• Suitable methodology
• Computer power (i.e. scientific instruments)

infrastructures in DH

• in hard sciences, infrastructures are tangible
  • servers, telescopes, accelerators, …
• in the humanities, institutions are essential
  • libraries, publishing houses, journals, …
• in Digital Humanities, multifaceted needs
  • the notion of infrastructure needs reconsideration
  • corpora (FAIR!) but not only

ELTeC corpus

CLS INFRA project

• text collections (corpora)
  • quality
  • metadata
  • conversion
• methodology
  • tools (NLP, datavis, …)
  • tool chains
  • methodological considerations
  • bibliographic survey
• network of scholars
  • training schools
  • short-term research stays
  • collaboration with COST Action

Overarching idea is to connect…

• People
  • To establish a network of CLS researchers
• Data
  • To consolidate existing high-quality corpora…
  • …covering prose, drama and poetry
• Tools
  • To build a chain of NLP tools to analyze texts
• Methods
  • To provide a survey of state-of-the-art methods

training schools

• Prague 2022
  • NLP tools
  • 25 participants on site
  • many more remotely
• Madrid 2023
  • text analysis
  • 10-11 May 2023
• Vienna 2024
  • corpus queries
  • 10-12 June 2024

TNA

• transnational access
• short-term research stays…
• in one of 6 institutions:
  • NUI Galway
  • Uni Potsdam
  • Uni Trier
  • UNED Madrid
  • OEAW Vienna
  • Charles Uni, Prague
• everyone eligible
• two calls every year

deliverables published

• 3.1 Report on the methodological baseline for (computational) literary studies
• 4.1 Report on the skills matrix for computational literary studies
• 5.1 Review of the data landscape
• 6.1 Assembly of existing data

survey of methods

survey of methods

• Representation of the current CLS research based on a large collection of publications
• Introduction to CLS research areas and prominent issues
• A guide for further reading

https://methods.clsinfra.io/

programmable corpora: DraCor

programmable corpora: DraCor

• DraCor: Highly functional prototype for an infrastructural ecosystem
• Programmable Corpora: research-driven API making text machine-actionable
• Open ecosystem: allowing for experimentation and discussion about architectural styles of research environments

https://dracor.org/

programmable corpora

• API Libraries developed in R and Python (published on the platforms PyPi an CRAN)
• Versioning:
  • Git commits for versioning and retrieving additional metadata
  • Docker containers of the entire research infrastructure - for more complex programmable corpora

tools and access for CLS

• list and description of Natural Language Processing (NLP) tools (Corpus-Enrichment and NLP toolchain for common CLS research tasks)
• increasing the ease of access and application to NLP tools, as well as their standardization

CLS INFRA legacy

• all outputs available online (freely!)
• plans to develop the infrastructure
• a network of scholars is growing

CLS-centric Discord server