Summary of the paper

Title A Regional News Corpora for Contextualized Entity Discovery and Linking
Authors Adrian Brasoveanu, Lyndon J.B. Nixon, Albert Weichselbraun and Arno Scharl
Abstract This paper presents a German corpus for Named Entity Linking (NEL) and Knowledge Base Population (KBP) tasks. We describe the annotation guideline, the annotation process, NIL clustering techniques and conversion to popular NEL formats such as NIF and TAC that have been used to construct this corpus based on news transcripts from the German regional broadcaster RBB (Rundfunk Berlin Brandenburg). Since creating such language resources requires significant effort, the paper also discusses how to derive additional evaluation resources for tasks like named entity contextualization or ontology enrichment by exploiting the links between named entities from the annotated corpus. The paper concludes with an evaluation that shows how several well-known NEL tools perform on the corpus, a discussion of the evaluation results, and with suggestions on how to keep evaluation corpora and datasets up to date.
Topics Corpus (Creation, Annotation, etc.), Named Entity Recognition, Evaluation Methodologies
Full paper A Regional News Corpora for Contextualized Entity Discovery and Linking
Bibtex @InProceedings{BRASOVEANU16.835,
  author = {Adrian Brasoveanu and Lyndon J.B. Nixon and Albert Weichselbraun and Arno Scharl},
  title = {A Regional News Corpora for Contextualized Entity Discovery and Linking},
  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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