Use this url to cite researcher: https://hdl.handle.net/20.500.12259/153963
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Amilevičius, Darius
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Amilevičius, Darius
Title
Mokslų daktaras (2010 m.) / Doctor of Sciences
Docentas / Associate Professor
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- journal article
; ; ; ; ; ; ; ; Darbai ir dienos = Deeds and days12 - book part
; ; ; ; ; ; Berlin : De Gruyter, 2022CLARIN-LT consortium is one of the leading Lithuanian language re-search and digital data storage infrastructures. This chapter will present outreach and initiatives performed by or in cooperation with the CLARIN-LT consortium and highlight their most significant outcomes. We will first highlight some of the resources stored in the CLARIN-LT repository and present their usage statistics. Next, we will show a use case of scientific outreach, followed by a success story involving the cooperation of large-scale national projects and CLARIN-LT in the development of IT services for Lithuanian. Finally, we will demonstrate an example of CLARIN content integration in university classes. The initiatives we overview here, although they have different aims and audiences, share one common feature – they all found a home at the CLARIN-LT repository. The presented use cases and success stories performed by or in cooperation with the CLARIN-LT consortium during the relatively short period of time since its establishment in 2015 show that the infra-structure is gaining recognition and is increasingly being addressed by scientific, educational, public, and private communities.34Scopus© Citations 4 ; ; Baltic journal of law & politicsThe pandemic affected the access to justice situation in terms of the never rapid shift to digitalisation of legal services, and in this article, we evaluate whether artificial intelligence (AI) and its state-of-the-art technologies like machine learning and human language technologies have the potential to improve access to legal services. For this purpose, we not only examine and identify problematic areas, but also share the empirical data and insights of the practical application of AI technologies, especially human language technologies. In the first part of the article, we explore how the internet has created the foundations for a new paradigm of society including institution law. The second part of the article is devoted for analysis of challenges for access to justice in post pandemic world. In the third part, we elaborate on questions about technical feasibility, legal and moral acceptability of the digitalisation of legal services. Then follows the case analysis of the practical application of human language technologies in legal domain.9Scopus© SNIP 1.296 - research article
; ; AI & Society: journal of knowledge, culture and communication. New York : Springer, 2021, Vol. 36, iss. 2, p. 547-560Artificial intelligence (AI) is one of the main drivers of what has been described as the “Fourth Industrial Revolution”, as well as the most innovative technology developed to date. It is a pervasive transformative innovation, which needs a new approach. In 2017, the European Parliament introduced the notion of the “electronic person”, which sparked huge debates in philosophical, legal, technological, and other academic settings. The issues related to AI should be examined from an interdisciplinary perspective. In this paper, we examine this legal innovation—that has been proposed by the European Parliament—from not only legal but also technological points of view. In the first section, we define AI and analyse its main characteristics. We argue that, from a technical perspective, it appears premature and probably inappropriate to introduce AI personhood now. In the second section, justifications for the European Parliament’s proposals are explored in contrast with the opposing arguments that have been presented. As the existing mechanisms of liability could be insufficient in scenarios where AI systems cause harm, especially when algorithms of AI learn and evolve on their own, there is a need to depart from traditional liability theories.64Scopus© Citations 4WOS© Citations 3Scopus© SNIP 1.295 36 26 55Scopus© SNIP 0.345 - research article
; ; Human language technologies - the Baltic perspective: proceedings of the 9th international conference, Baltic HLT, Kaunas, Vytautas Magnus University, Lithuania, 22-23 September 2020 / editors Andrius Utka, Jurgita Vaičenonienė, Jolanta Kovalevskaitė, Danguolė Kalinauskaitė. Amsterdam : IOS Press, 2020, p. 32-38The paper presents research results for solving the task of targeted aspect-based sentiment analysis in the specific domain of Lithuanian social media reviews. Methodology, system architecture, relevant NLP tools and resources are described, finalized by experimental results showing that our solution is suitable for solving targeted aspect-based sentiment analysis tasks for under-resourced, morphologically rich and flexible word order languages.62 100Scopus© SNIP 0.338 ; 72 ; DAMSS-2018: Data analysis methods for software systems: 10th international workshop, Druskininkai, Lithuania, November 29 – December 1, 2018: [book of abstract], p. 26-26As application of artificial intelligence in different domains becomes more ubiquitous, scholars raise questions on ethical considerations with regard to privacy, decision bias, algorithmic transparency and accountability. Theoretically, machines are supposed to deliver unbiased decisions. Recent examples, however, show the contrary – even algorithmic mind can be prejudiced. One typical example of bias in AI is that of gender in language translations. To assure AI functionality delivers bias-free results, the underlying machine learning process must be properly managed from input to output – including data, algorithms, models, training, testing and predictions – to make sure that bias is not perpetuated. Speakers of grammatically genderless language may have impression that it is superior compared to grammatical gender language, as they supposedly genderless character is seen as an expression of gender equality. But a lack of grammatical gender does not automatically reflect a more gender-neutral society. Due to the absence of grammatical gender femaleness and maleness can only be expressed through lexical and socially gendered forms. Morphologically gender-neutral nouns often carry a hidden cultural or social gender bias. In Lithuanian, a male noun has two referential functions: a male-specific and generic function. For this reason, the former case goes close to bias in genderless languages. In our research we detected the presence of bias in Lithuanian text which links women to less prestigious jobs, while men – to more prestigious ones. The problem is not related to the morphology. We used word embedding (word2vec and FastText) and debiased word embedding methods. The result is that debiased word embedding method reducing gender bias is more effective than bias finetuning method. [...]6 63 - conference paper
; ; DAMSS-2018: Data analysis methods for software systems: 10th international workshop, Druskininkai, Lithuania, November 29 – December 1, 2018: [book of abstract]. Vilnius: Vilnius University Press, 2018, p. 67-67Opinion mining (sentiment analysis) problem is usually solved by applying a lexicon-based model (e.g., in www.semantika.lt project). Deep neural networks are gaining popularity in different text processing and classification tasks, sentiment analysis being one of them. The paper presents research on deep-learning algorithms for aspect-based sentiment analysis, aiming at sentiment definition for each identified aspect of a given entity (e.g., a person, a brand name, etc.). Most of the distributional word embedding models nowadays learn semantic representations of words ignoring their morphological structure. This appears to be a limitation, especially for languages with complex morphology, as their vocabularies are quite large, most words are infrequent, resulting in models being unable to learn acceptable semantic representations. The paper presents the research results on the application of a FastText n-gram (subword) based word embedding model, treating each word as a composition of character n-grams (subwords). Subword-level information is crucial for capturing word meaning and morphology, especially for out-of-vocabulary (OOV) entries and rare words. Comparison with the traditional word2vec (Skip-Gram) word embedding model showed, that the subword feature enhances learning for morphologically rich, heavily inflected languages, such as the Lithuanian language. It is shown that for Lithuanian texts FastText models with n-grams do significantly better on syntactic tasks (aspect-sentiment pair), because of the syntactic questions being related to morphology of the words. However, original word2vec models perform better on semantic tasks, since words in semantic analogues (distributional hypothesis) are unrelated to their n-grams, and the add-on information from irrelevant character n-grams can worsen the embeddings. [...]26
