We present a first empirical effort in annotating attribution in Modern Standard Arabic (MSA). Identifying attributed arguments to the source is applied successfully in diverse systems such as authorship identification, information retrieval, and opinion mining. Current studies focus on using lexical terms in long texts to verify, for example, the author identity. While attribution identification in short texts is still unexplored completely due to the lack of resources such as annotated corpora and tools especially in Arabic on one hand, and the limited coverage of different attribution usages in Arabic literature, on other hand. The paper presents our guidelines for annotating attribution elements: cue, source, and the content with required syntactical and semantic features in Arabic news (Arabic TreeBank - ATB) insight of earlier studies for other languages with all required adaptation. We also develop a new annotation tool for attribution in Arabic to ensure that all instances of attribution are reliably annotated. The results of a pilot annotation are discussed in addition to the inter-annotators agreement studies towards creating the first gold standard attribution corpus for Arabic.
@InProceedings{ALSAIF18.83, author = {Amal Alsaif and Tasniem Alyahya and Madawi Alotaibi and Huda Almuzaini and Abeer Algahtani}, title = "{Annotating Attribution Relations in Arabic}", 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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