Summary of the paper

Title The Royal Society Corpus: From Uncharted Data to Corpus
Authors Hannah Kermes, Stefania Degaetano-Ortlieb, Ashraf Khamis, Jörg Knappen and Elke Teich
Abstract We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665―1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.
Topics Corpus (Creation, Annotation, etc.), Knowledge Discovery/Representation, Metadata
Full paper The Royal Society Corpus: From Uncharted Data to Corpus
Bibtex @InProceedings{KERMES16.792,
  author = {Hannah Kermes and Stefania Degaetano-Ortlieb and Ashraf Khamis and Jörg Knappen and Elke Teich},
  title = {The Royal Society Corpus: From Uncharted Data to Corpus},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
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