We present intertextual correspondence (ITC) as an integrative technique for combining annotated text corpora. The topical correspondence between different texts can be exploited to establish new annotation connections between existing corpora. Although the general idea should not be restricted to one particular theoretical framework, we explain how the annotation of intertextual correspondence works for two corpora annotated with argumentative notions on the basis of Inference Anchoring Theory. The annotated corpora we take as examples are topically and temporally related: the first corpus comprises television debates leading up to the 2016 presidential elections in the United States, the second corpus consists of commentary on and discussion of those debates on the social media platform Reddit. The integrative combination enriches the existing corpora in terms of the argumentative density, conceived of as the number of inference, conflict and rephrase relations relative to the word count of the (sub-)corpus. ITC also affects the global properties of the corpus, such as the most divisive issue. Moreover, the ability to extend existing corpora whilst maintaining the level of internal cohesion is beneficial to the use of the integrated corpus as resource for text and argument mining based on machine learning.
@InProceedings{VISSER18.1087, author = {Jacky Visser and Rory Duthie and John Lawrence and Chris Reed}, title = "{Intertextual Correspondence for Integrating Corpora}", 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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