How tight is your language? A semantic typology based on Mutual Information

Languages differ in the degree of semantic flexibility of their syntactic roles. For example, Eng- lish and Indonesian are considered more flexible with regard to the semantics of subjects, whereas German and Japanese are less flexible. In Hawkins’ classification, more flexible lan- guages are said to have a loose fit, and less flexible ones are those that have a tight fit. This classification has been based on manual inspection of example sentences. The present paper proposes a new, quantitative approach to deriving the measures of looseness and tightness from corpora. We use corpora of online news from the Leipzig Corpora Collection in thirty typolog- ically and genealogically diverse languages and parse them syntactically with the help of the Universal Dependencies annotation software. Next, we compute Mutual Information scores for each language using the matrices of lexical lemmas and four syntactic dependencies (intransi- tive subjects, transitive subject, objects and obliques). The new approach allows us not only to reproduce the results of previous investigations, but also to extend the typology to new lan- guages. We also demonstrate that verb-final languages tend to have a tighter relationship be- tween lexemes and syntactic roles, which helps language users to recognize thematic roles early during comprehension

Baltijas Psiholoģijas žurnāls

Contents: Viesturs Reņģe, Ivars Austers. Social Representations of Science and Psychology: Anchoring and Personification ; Aleksandrs Koļesovs. Gender Differences in Time Perspective of High School Students in Latvia ; Daina Škuškovnika. Comparison of State and Trait Anxiety of Latvians and Russians Residing in Latvia ; Mary Balaisis, Juris Dragūns, Solveiga Miezītis. Students’ Adjustment at Vilnius University: The Role of Self-Orientation, Locus of Control, Social Support and Demographic Variable

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Literacy in Lithuania. Country Report - Children and Adolescents

European Literacy policy network (ELINET

How Do Students Studying Turkish in Lithuania Describe Turkish Culture and People?

The aim of this paper is to describe ideas that students learning Turkish in Lithuania have about Turkish culture and Turkish people. A descriptive method was used in the research. The data for the research was collected from 15 students who learn Turkish at Vilnius University in Vilnius, Lithuania. For the participating in the research students, “An Emotional Meaning Scale about Turkish Culture and Turkish People” was used. The students were asked to identify positive and negative attributes connected with Turkish culture and Turkish people. The data was grouped according to frequency (f) values and interpreted accordingly. The research revealed that the students evaluate Turkish culture and Turkish people positively and that the positive ideas belong to a high level. Keywords: Teaching Turkish to foreigners, Lithuania, Turkish culture, Turkish people.

“Spotting the signs” of trafficking recruitment online:Exploring the characteristics of advertisements targeted at migrant job-seekers

Despite considerable concern about how human trafficking offenders may use the Internet to recruit their victims, arrange logistics or advertise services, the Internet-trafficking nexus remains unclear. This study explored the prevalence and correlates of a set of commonly-used indicators of labour trafficking in online job advertisements. Taking a case study approach, we focused on a major Lithuanian website aimed at people seeking work abroad. We examined a snapshot of job advertisements (n = 430), assessing both their general characteristics (e.g. industry, destination country) and the presence of trafficking indicators. The vast majority (98.4%) contained at least one indicator, suggesting certain "indicators" may in fact be commonplace characteristics of this labour market. Inferential statistical tests revealed significant but weak relationships between the advertisements’ characteristics and the number and nature of indicators present. While there may be value in screening job advertisements to identify potential labour trafficking and exploitation, additional information is needed to ascertain actual labour trafficking. We conclude with an outlook on automated approaches to identifying cases of possible trafficking and a discussion of the benefits and ethical concerns of a data science-driven approach