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This paper presents work-in-progress of the POSTDATA project. This project aims to provide means to solve the interoperability issues that exist among the digital poetry repertoires. These repertoires hold data of poetry metrics that is locked in their own databases and it is not freely available to be compared and to be used by intelligent machines that could infer over the data. The POSTDATA project will use Linked Open Data (LOD) technologies to overcome the interoperability problems. POSTDATA is developing a metadata application proFIle (MAP) for the digital poetry repertoires, a construct that enhances interoperability.This development follows the method for the development of MAP (Me4MAP).A MAP for the digital poetry repertoires will open doors for this repertoires to be able to structure the data with a common model in order to publish it as Linked Open Data. This paper presents how this MAP is being developed so far. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

Digital Humanities researchers often make use of software that helps them in the task of finding non-trivial relationships among characters in historical text. Usually, the source texts that contain such information come from OCR acquired volumes, carrying high amounts of errors within them. This work explains the development of a web platform for the task of OCR post-processing and ground-truth generation. This platform employs machine learning to predict the correct texts accurately from OCR noisy strings. The method used for this task involves transformers for character-based denoising language models. An active learning workflow is proposed, as the users can feed their corrections to the platform, generating new annotated data for re-training the underlying machine learning correction models. Sociedad Argentina de Informática e Investigación Operativa