Summary of the paper

Title Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.
Authors Jon Chamberlain, Massimo Poesio and Udo Kruschwitz
Abstract Natural Language Engineering tasks require large and complex annotated datasets to build more advanced models of language. Corpora are typically annotated by several experts to create a gold standard; however, there are now compelling reasons to use a non-expert crowd to annotate text, driven by cost, speed and scalability. Phrase Detectives Corpus 1.0 is an anaphorically-annotated corpus of encyclopedic and narrative text that contains a gold standard created by multiple experts, as well as a set of annotations created by a large non-expert crowd. Analysis shows very good inter-expert agreement (kappa=.88-.93) but a more variable baseline crowd agreement (kappa=.52-.96). Encyclopedic texts show less agreement (and by implication are harder to annotate) than narrative texts. The release of this corpus is intended to encourage research into the use of crowds for text annotation and the development of more advanced, probabilistic language models, in particular for anaphoric coreference.
Topics Anaphora, Coreference, Crowdsourcing, Corpus (Creation, Annotation, etc.)
Full paper Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.
Bibtex @InProceedings{CHAMBERLAIN16.295,
  author = {Jon Chamberlain and Massimo Poesio and Udo Kruschwitz},
  title = {Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.},
  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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