%0 Conference Proceedings %T PARC 3.0: A Corpus of Attribution Relations %A Pareti, Silvia %Y Calzolari, Nicoletta %Y Choukri, Khalid %Y Declerck, Thierry %Y Goggi, Sara %Y Grobelnik, Marko %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Helene %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16) %D 2016 %8 May %I European Language Resources Association (ELRA) %C Portorož, Slovenia %F pareti-2016-parc %X Quotation and opinion extraction, discourse and factuality have all partly addressed the annotation and identification of Attribution Relations. However, disjoint efforts have provided a partial and partly inaccurate picture of attribution and generated small or incomplete resources, thus limiting the applicability of machine learning approaches. This paper presents PARC 3.0, a large corpus fully annotated with Attribution Relations (ARs). The annotation scheme was tested with an inter-annotator agreement study showing satisfactory results for the identification of ARs and high agreement on the selection of the text spans corresponding to its constitutive elements: source, cue and content. The corpus, which comprises around 20k ARs, was used to investigate the range of structures that can express attribution. The results show a complex and varied relation of which the literature has addressed only a portion. PARC 3.0 is available for research use and can be used in a range of different studies to analyse attribution and validate assumptions as well as to develop supervised attribution extraction models. %U https://aclanthology.org/L16-1619 %P 3914-3920