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Publisher Copyright: © 2021 Estonian Association Applied Linguists. All rights reserved. The study focuses on the sharing of linguistic data containing personal data, which is the processing of personal data. Therefore, the requirements of the General Data Protection Regulation (GDPR) must be complied with. Compliance with these requirements is the responsibility of the controller, who may use the assistance of processors to process the data. In international practice, it is not clear how the responsibility for the processing of personal data is divided between a specific researcher and a research institution. For example, the French and German models differ from the Estonian, Lithuanian and Finnish models. The Greek model offers a unique approach. In general, the employer (organization or legal entity) is responsible for the processing of personal data. At the same time, research has its own specificity, which is characterized by academic freedom and as well as mobility of researchers. The situation is further complicated by the sharing of language data through research networks such as CLARIN. In the present study, the authors analyze the division of duties and responsibilities between the researcher and the research institution (including research networks). The authors also analyze how data sharing should take place from a personal data protection perspective. It is important to clarify whether the data provider and the data recipient are both controllers, joint controllers or the recipient is the processor and how the responsibility for data sharing is shared. The authors of the analysis have an interdisciplinary and international background, covering different areas of law (data protection, labor law, contract law) and jurisdictions (Estonia, Italy, Greece, Lithuania, France, Germany, Finland) and language technology.

Kirjandusteadlaste ja arvutilingvistide koostöös katseprojektina valminud Johannes Semperi ja Johannes Barbaruse kirjavahetuse korpus on nii kirjanduslooliselt kui tekstilingvistiliselt huvipakkuv digitaalandmestik. Kirjandusteadlastele avab kaasaegsete digitaalsete meetodite kasutuselevõtt huvitavaid uurimisperspektiive ja vanade uurimistulemuste ülekontrollimise võimalusi arvutuslike meetoditega. Korpuslingvistidele on aga väljakutseks ajaloolise ja isikupärase keelekasutusega, erinevatest keeltest kubiseva ja rohkete koha-, aja- ja isikuviidetega tekstimaterjali ettevalmistamine rikkalikult märgendatud korpuseks. Artikkel peatub üksikasjalikumalt nii käsikirjalise materjali digitaalseks tekstiandmestikuks ettevalmistamise kui ka analüüsi- ja märgendamisprotsessi probleemidel ja nende võimalikel lahendustel. Kasutajatele tutvustatakse ka korpuste päringusüsteemi KORP võimalusi sarnaste tekstide uurimiseks. Abstract. Marin Laak, Kaarel Veskis, Kadri Vider, Neeme Kahusk, and Olga Gerassimenko: Turning from digital document to text corpus: conversion of correspondence between Semper and Barbarus to a machine-readable unit in KORP. The article describes a joined pilot project of literary scholars and language technologists that resulted in a correspondence corpus of Estonian avant-garde poets Johannes Semper and Johannes Barbarus. The corpus is an inspiring digital dataset both for literary and linguistic researches. Contemporary digital methods allow literary scholars to find new interesting research perspectives and to revise the old research results with computational methods. Corpus linguists can find interesting challenges in historically and personally unique language use of the correspondents, in multiple languages used for citations and language play, in multiple references to places, events and persons in the textual material that was transformed to an annotated corpus. The article describes the preparation of typed-in manuscript material for a digital dataset in detail, problems of annotation and analysis and their possible solutions. The reader will get an insight to the possibilities that corpus query system KORP offers for the research of similar textual material.

7th International Conference of the Faculty-of-Law-of-the-University-of-Latvia on Legal Science - Functions,Significance and Future in Legal Systems, Riga, LATVIA, OCT 16-18, 2019 The purpose of this article is to explain the extent to which the legal regime applicable to language data affects the development and use of language technology (LT). The main focus of the paper is on EU law. The article also maps possible text and data mining (TDM) issues. The authors focus on TDM for research purposes outlined in the Digital Copyright Directive 2019/790.The authors follow a process approach of LT development, which starts from raw data collection and leads to LT products such as a refrigerator with a speech interface. Particular attention is given to language models.The raw data used in LT often include copyright-protected works, objects of related rights (e.g., performances) and personal data in the form of person’s voice or other information stored in non-annotated and annotated databases.The authors’ main argument is that the legal regime of language data does not usually affect the use of language models since copyrighted works are not likely to remain in models. In the process of developing a language technology application, language models are the first intermediate result that can be free from legal restrictions affecting language data. The use of a person’s voice as identifiable personal data in a language model can create legal challenges. In some cases, developers of language technology must be careful how to address issues of processing of personal data contained in models.

The authors address the transformation of research data into open data. The article draws on the experience in four countries: Sweden, Finland, Estonia and Lithuania. The transformation process presents several challenges where legal, organizational and individual aspects influence the process. Research data often contain personal data. Research data could also be covered with intellectual property (IP) rights. This means that personal data and IP regulations should be integrated into the dissemination model. While there is a potential conflict between the policies for open data that aim to make data freely available and those of an entrepreneurial university that emphasize commercialization of research results, these policies need to be made compatible. Researchers producing data are vital for reconciling the two, but they currently lack the motivation to contribute towards the implementation of the open data policy due to missing career incentives.

Kui keeleressursside autorioiguslikke kusimusi on eelnevalt pohjalikult uuritud, siis isikuandmete kaitse temaatika keeleressursside kontekstis on suurelt osalt katmata. Teema on aktuaalne, sest 25. mail 2018 hakatakse kohaldama EL-i isikuandmete kaitse uldmaarust. Kaesolevas artiklis analuusitakse isikuandmete kaitse kusimusi keeleressursside loomisel ja kasutamisel teadustoo eesmargil kahes kontekstis: 1) andmesubjekt ehk isik, kelle andmed keeleressursis sisalduvad, on andnud oma andmete tootlemiseks nousoleku; 2) nousolekut ei ole ning isikuandmete tootlemine toimub teadustoo erandi alusel. Artiklis analuusitakse keeleressursside loomisel kasutuses olevaid nousolekuvorme ning tehakse ettepanekuid, kuidas keeleressursside kontekstis isikuandmete kaitse regulatsiooni jargida. *** Data protection issues relating to the development and utilisation of language resources Copyright issues related to language resources have received considerable attention. Personal data protection in the field of language research is an area which is not thoroughly explored yet. This topic is very relevant since the General Data Protection Regulation is applicable from 25 May 2018. The authors analyse the issues relating to personal data protection in the context of the development and utilization of language resources. Firstly, the authors analyse the cases where there is a consent which allows processing of personal data to develop and use language resources for research purposes. Secondly, the processing of personal data without consent for language research purposes is explored. The article is meant to provide practical information to language research community and create a foundation for further discussion.

Artikkel käsitleb teaduspoliitilisi ja õiguslikke küsimusi, mida tuleks arvestada keeleressursside litsentsimisel, ja määratleb sobivaimad litsentsimise tingimused, pöörates tähelepanu eelkõige lepinguõiguse ja intellektuaalomandi litsentsimise küsimustele. Samuti võrreldakse artiklis USA, Euroopa riikide ja Eesti keeleressursside litsentsimise õiguslikku raamistikku ning tutvustatakse levinumaid praktikaid avatud juurdepääsu (ingl open access) ja vabade (avatud) litsentside vaatepunktist. Artikli praktiliseks eesmärgiks on selgitada Eestis keeleressursside loomise ja kasutamisega seotud teadlastele ja teadusasutustele intellektuaalomandi (IO) objektide litsentsimise põhialuseid ja jagada näpunäiteid litsentsitingimuste valimiseks. Artikkel on sündinud keeleteadlaste ja juristide koostöös ning mõeldud esmajoones keeleteadlastest lugejaskonnale.DOI: http://dx.doi.org/10.5128/ERYa9.21

The chapter deals with the analysis of sentence structure on the layer where semantic role categories such as AGENT, PATIENT, OBJECT, etc. play a crucial role. The main objective is to find out which regularities prevail in categorization of spatial characteristics of motion events in Estonian in terms of these categories, and which morphosyntactic means are used to encode them. For the analysis a (mini) corpus containing sentences with a motion verb as predicate was automatically created. First, a brief overview of verbs of motion in Estonian is given. After that, the chapter concentrates on the analysis of semantic roles and their encoding in motion sentences in Estonian. We found it necessary to differentiate between the following roles: SOURCE, GOAL, ROUTE, LOCATION. The realization of each category by morphosyntactic means of Estonian is described in detail: NPs in certain case forms, PPs, adverbs. One of the results of the chapter is the fact that there is a statistical asymmetry in the use of SOURCE, GOAL, and LOCATION: GOAL is the most frequently encoded category of these three.