We describe the MAPA project, funded under the Connecting Europe Facility programme, whose goal is the development of an open-source de-identification toolkit for all official European Union languages. It will be developed since January 2020 until December 2021.

The Voynich Manuscript has baffled scholars for centuries. Some believe the elaborate 15th century codex to be a hoax whilst others believe it is a real medieval manuscript whose contents are as yet unknown. In this paper, we provide additional evidence that the text of the manuscript displays the hallmarks of a proper natural language with respect to the relationship between word probabilities and (i) average information per subword segment and (ii) the relative positioning of consecutive subword segments necessary to uniquely identify words of different probabilities.

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by “MWE processing,” distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives.

Bilingual dictionaries define word equivalents from one language to another, thus acting as an important bridge between languages. No bilingual dictionary is complete since languages are in a constant state of change. Additionally, dictionaries are unlikely to achieve complete coverage of all language terms. This paper investigates methods for extending dictionaries using non-aligned corpora, by finding translations through context similarity. Most methods compute word contexts from general corpora. This can lead to errors due to data sparsity. We investigate the hypothesis that this problem can be addressed by carefully choosing smaller corpora in which domain-specific terms are more predominant. We also introduce the notion of efficiency which we consider as the effort required to obtain a set of dictionary entries from a given corpus

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.

This paper discusses the ongoing development of a new Maltese spell checker, highlighting the methodologies which would best suit such a language. We thus discuss several previous attempts, highlighting what we believe to be their weakest point: a lack of attention to context. Two developments are of particular interest, both of which concern the availability of language resources relevant to spellchecking: (i) the Maltese Language Resource Server (MLRS) which now includes a representative corpus of c. 100M words extracted from diverse documents including the Maltese Legislation, press releases and extracts from Maltese web-pages and (ii) an extensive and detailed corpus of spelling errors that was collected whilst part of the MLRS texts were being prepared. We describe the structure of these resources as well as the experimental approaches focused on context that we are now in a position to adopt. We describe the framework within which a variety of different approaches to spellchecking and evaluation will be carried out, and briefly discuss the first baseline system we have implemented. We conclude the paper with a roadmap for future improvements.

Plain text corpora contain much information which can only be accessed through human annotation and semantic analysis, which is typically very time consuming to perform. Analysis of such texts at a syntactic or grammatical structure level can however extract some of this information in an automated manner, even if identifying effective rules can be extremely difficult. One such type of implicit information present in texts is that of definitional phrases and sentences. In this paper, we investigate the use of evolutionary algorithms to learn classifiers to discriminate between definitional and non-definitional sentences in non-technical texts, and show how effective grammar-based definition discriminators can be automatically learnt with minor human intervention.

This paper describes ODL, a description language for lexical information that is being developed within the context of a national project called MLRS (Maltese Language Resource Server) whose goal is to create a national corpus and computational lexicon for the Maltese language. The main aim of ODL is to make the task of the lexicographer easier by allowing lexical specifications to be set out formally so that actual entries will conform to them. The paper describes some of the background motivation, the ODL language itself, and concludes with a short example of how lexical values expressed in ODL can be mapped to an existing tagset together with some speculations about future work.