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International audience; This paper presents the outcomes of the MAPA project, a set of annotated corpora for 24 languages of the European Union and an open-source customisable toolkit able to detect and substitute sensitive information in text documents from any domain, using state-of-the art, deep learning-based named entity recognition techniques. In the context of the project, the toolkit has been developed and tested on administrative, legal and medical documents, obtaining state-of-the-art results. As a result of the project, 24 dataset packages have been released and the de-identification toolkit is available as open source.

The European MAPA (Multilingual Anonymisation for Public Administrations) project aims at developing an open-source solution for automatic de-identification of medical and legal documents. We introduce here the context, partners and aims of the project, and report on preliminary results., peer-reviewed

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented, by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 National Competence Centres (NCCs) and the European LT Council (LTC) for outreach and coordination purposes., Comment: Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020). To appear

Europe is a multilingual society with 24 EU Member State languages and dozens of additional languages including regional and minority languages and languages spoken by immigrants and trade partners. While languages are an essential part of our cultural heritage, language barriers continue to be unbreachable in many situations. The only option to enable and to benefit from multilingualism is through Language Technologies (LT) including Natural Language Processing (NLP) and Speech Technologies. The commercial European LT landscape is dominated by hundreds of SMEs. While the academic and industrial European LT community is world class, it is also massively fragmented. We describe the European Language Grid (ELG), Release 2. The system is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to showcase, share and distribute their services, products and resources. At the end of our EU-funded project, which will establish a legal entity in late 2021, the ELG will provide access to approx. 1300 commercial and non-commercial services for all European languages, as well as thousands of language resources (LRs). ELG will enable the whole European LT community to upload their technologies and resources and to deploy them through the ELG. The ELG is also meant to support digital language equality in Europe, i.e., to foster a situation in which all languages are supported through technologies equally well. We currently still have an extreme imbalance, characterised by a stark predominance of LTs for English.

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented – by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 national competence centres and the European LT Council for outreach and coordination purposes.

This report elaborates on the exploitation of the PANACEA project assets. These assets have been clustered into a few items (a) the PANACEA Factory/Platform, (b) the web services integrated within the platform, (c) the associated workflows to manage the sequencing of web services (d) the tools developed during the project and last but not least (e) the data sets, i.e. Language Resources (LRs) produced mainly for Machine translation/localization, but not only, within the project, exploiting the platform, web services, and the workflows.

The PANACEA user’s workshop took place in Budapest on June 29, as a satellite event of the META-FORUM 2011. The choice for this collocated event was indeed strategic. META-FORUM is an event organised by another FP7 project (T4ME/META-NET) that shares a strong interest for the industrial world with PANACEA. This seemed very interesting in order to attract potential users’ interest and encourage them to join us for the workshop. Targeting two events was certainly a plus for some of the attendees.

This deliverable reports on the first evaluation cycle consisting of: 1) the validation of the platform, i.e. the integration of components; and 2) the evaluation of the components that produce resources, and, therefore, of the resources produced. The methodology and criteria for the evaluation of the technology integrated into the platform and for the validation of the integration of components have been described in D7.1. Some of the/ncriteria involved in this evaluation cycle will be repeated here for the reader’s sake.

This deliverable defines how evaluation will be carried out at each integration cycle. As PANACEA aims at producing large scale resources, evaluation becomes a critical and challenging issue. Critical because it is important to assess the quality of the results that should be delivered to users. Challenging because we/nprospect rather new areas, and through a technical platform: some new methodologies will have to be explored or old ones to be adapted.

This report describes the deliverable D5.3 of the PANACEA project: parallel, sententially aligned texts, cleaned and prepared for training-building translation models. This deliverable is/nan outcome of the work packages WP4.1, WP4.2, and WP5.1. The resulting domain-specific parallel corpus includes a total of 4 million words for two language pairs: English–French and/nEnglish–Greek and two domains: Environment and Labour Legislation. The data for each language pair and domain is split into three sets: training data for building translation models, development data for development purposes, and test data for testing and evaluation.

This deliverable document explains the creation process of monolingual corpora that were created with the first version of the Corpus Acquisition and Annotation subsystem (CAA). The first version of the CAA was developed during the first year of the project following insights from D4.1 Technologies and tools for corpus creation, normalization and annotation (T6). The main component currently integrated in the CAA is the Corpus Acquisition Component (CAC). Although the CAC is described in more detail in D4.2 Initial functional prototype and documentation (due T13), we include extracts from that document in the present deliverable, when we believe that this would help the reader understand the corpus acquisition process we followed.

The objective of the PANACEA ICT-2007.2.2 EU project is to build a platform that automates the stages involved in the acquisition, production, updating and maintenance of the large language resources required by, among others, MT systems. The development of a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web is one of the most innovative building blocks of PANACEA. The CAC, which is the first stage in the PANACEA pipeline for building Language Resources, adopts an efficient and distributed methodology to crawl for web documents with rich textual content in specific languages and predefined domains. The CAC includes modules that can acquire parallel data from sites with in-domain content available in more than one language. In order to extrinsically evaluate the CAC methodology, we have conducted several experiments that used crawled parallel corpora for the identification and extraction of parallel sentences using sentence alignment. The corpora were then successfully used for domain adaptation of Machine Translation Systems., 3 pages. Also available in UPF institutional repository (http://hdl.handle.net/10230/20416)

This paper presents the metadata schema for describing language resources (LRs) currently under development for the needs of META-SHARE, an open distributed facility for the exchange and sharing of LRs. An essential ingredient in its setup is the existence of formal and standardized LR descriptions, cornerstone of the interoperability layer of any such initiative. The description of LRs is granular and abstractive, combining the taxonomy of LRs with an inventory of a structured set of descriptive elements, of which only a minimal subset is obligatory; the schema additionally proposes recommended and optional elements. Moreover, the schema includes a set of relations catering for the appropriate inter-linking of resources. The current paper presents the main principles and features of the metadata schema, focusing on the description of text corpora and lexical / conceptual resources.

The documentation, protection and dissemination of Intangible Cultural Heritage (ICH) in the digital age pose significant theoretical, technological and legal challenges. Through a multidisciplinary lens, this paper presents new approaches for collecting, documenting, encrypting and protecting ICH-related data for more ethical circulation. Human-movement recognition technologies such as motion capture, allows for the recording, extraction and reproduction of human movement with unprecedented precision. The once indistinguishable or hard-to-trace reproduction of dance steps between their creators and unauthorized third parties becomes patent through the transmission of embodied knowledge, but in the form of data. This new battlefield prompted by digital technologies only adds to the disputes within the creative industries, in terms of authorship, ownership and commodification of body language. For the sake of this paper, we are aiming to disentangle the various layers present in the process of digitisation of the dancing body, to identify its by-products as well as the possible arising ownership rights that might entail. ”Who owns what?”, the basic premise of intellectual property law, is transposed, in this case, onto the various types of data generated when intangible cultural heritage, in the form of dance, is digitised through motion capture and encrypted with blockchain technologies.