An attribution occurs when an author quotes, paraphrases, or describes the statements and private states of a third party. Journalists use attribution to report statements and attitudes of public figures, organizations, and ordinary individuals. Properly recognizing attributions in context is an essential aspect of natural language understanding and implicated in many NLP tasks, but current resources are limited in size and completeness. We introduce the Political News Attribution Relations Corpus 2016 (PolNeAR)---the largest, most complete attribution relations corpus to date. This dataset greatly increases the volume of high-quality attribution annotations, addresses shortcomings of existing resources, and expands the diversity of publishers sourced. PolNeAR is built on news articles covering the political candidates during the year leading up to US Presidential Election in November of 2016. The dataset will support the creation of sophisticated end-to-end solutions for attribution extraction and invite interdisciplinary collaboration between the NLP, communications, political science, and journalism communities. Along with the dataset we contribute revised guidelines aimed at improving clarity and consistency in the annotation task, and an annotation interface specially adapted to the task, for reproduction or extension of this work
@InProceedings{NEWELL18.1051, author = {Edward Newell and Drew Margolin and Derek Ruths}, title = "{An Attribution Relations Corpus for Political News}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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