Parliamentary debates constitute a substantial and somewhat underutilized reservoir of publicly available written content. Despite their potential, the Italian parliamentary documents remain largely unexplored and most importantly inaccessible in their original paper-based form. In this paper we attempt to transform these valuable historical documents into IPSA, a digitally readable structured corpus containing speeches, reports of the Standing Committees, and law proposals spanning 175 years of Italian history, from the issuing of the Statuto Albertino in 1848, up to the present day. At first, the PDF documents, available on the official websites of Senato della Repubblica and Camera dei Deputati, the two chambers that form the Italian Parliament, are digitized using Optical Character Recognition (OCR) techniques. Then, the speeches are tagged with the corresponding speakers. The final dataset is released both in textual and structured format.

Amazon Mechanical Turk (AMT) has recently become one of the most popular crowd-sourcing platforms, allowing researchers from all over the world to create linguistic datasets quickly and at a relatively low cost. Amazon provides both a web interface and an API for AMT, but they are not very user-friendly and miss some features that can be useful for NLP researchers. In this paper, we present EasyTurk, a free tool that improves the potential of Amazon Mechanical Turk by adding to it some new features. The tool is free and released under an open source license.

In this paper, we present Tintful, an NLP annotation software that can be used both to manually annotate texts and to fix mistakes in NLP pipelines, such as Stanford CoreNLP. Using a paradigm similar to wiki-like systems, a user who notices some wrong annotation can easily fix it and submit the resulting (and right) entry back to the tool developers. Moreover, Tintful can be used to easily annotate data from scratch. The input documents do not need to be in a particular format: starting from the plain text, the sentences are first annotated with CoreNLP, then the user can edit the annotations and submit everything back through a user-friendly interface.