Magistro baigiamojo darbo tikslas - ištirti maisto produktų ženklinimo ir vartotojų teisių apsaugos teisino reglamentavimo ypatumus. Tam, kad būtų pasiektas numatytas tikslas, darbe buvo iškelti šie pagrindiniai uždaviniai: išanalizuoti maisto produktų ženklinimo teisinio reglamentavimo ypatumus Europos Sąjungos ir Lietuvos teisės aktuose; išnagrinėti reikalavimų ženklinant ekologiškus ir maisto priedų turinčius maisto produktus teisinį reguliavimą; išnagrinėti vartotojo galimybes neteisminiu keliu realizuoti savo teises maisto produktų ženklinimo srityje. Vienas iš Europos Sąjungos maisto saugos politikos tikslų – užtikrinti, kad maisto produktai būtų ženklinami. Todėl pirmame darbo skyriuje išanalizavus maisto produktų ženklinimo teisinį reglamentavimą galima teigti, jog šios srities ženklinimas Europos Sąjungoje yra reglamentuojamas horizontaliais ir vertikaliais teisės aktais. Naujuoju Europos Parlamento ir Tarybos 2011 m. spalio 25 d. reglamentu Nr. 1169/2011 siekiama sujungti šiuo metu galiojančius teisės aktus dėl maisto produktų ženklinimo, juos konsoliduoti bei supaprastinti. Pagrindiniu teisiniu dokumentu, reglamentuojančiu maisto produktų ženklinimą Lietuvos Respublikoje, yra laikoma Lietuvos higienos norma HN 119:2002 „Maisto produktų ženklinimas“. Plečiantis tiek ekologiškų, tiek maisto priedų turinčių maisto produktų rinkai, antrame darbo skyriuje nagrinėjami teisės aktų keliami reikalavimai šių produktų ženklinimui. Teisės aktų, reglamentuojančių ekologiškų... [toliau žr. visą tekstą] / The goal of this master thesis is to complete a research on the legal regulation peculiarities of food labelling and consumer protection. In order to achieve the set goal, following tasks were formed in this thesis: to analyze the legal regulation peculiarities of food labelling in legislation of European Union and Lithuania; to analyze legal regulation of requirements for labelling organic food and products that contain food additives; to provide insights regarding possibilities for consumers to implement their rights in the sphere of food labelling. One of the goals of the European Union food safety policy is to ensure the labelling of food. Therefore, after analysing the legal regulation of food labelling in the first section of the thesis, a proposition can be made that, the labelling of this sphere in the European Union is regulated by horizontal and vertical legislation. The new regulation No. 1169/2011, issued by the European Parliament and Council on the 25th of October, seeks to join, consolidate and simplify currently valid legislation regarding food labelling. The main legal document which regulates food labelling in the Republic of Lithuania is Lithuanian Hygiene Norm HN 119:2002 “Food Labelling”. While the market of both organic food and products that contain additives continues to develop, the second section of the thesis analyzes the requirements set by legislation for labelling such products. The analysis of legislation that regulates the labelling of... [to full text]

Structured Product Labels (SPLs), the package inserts that accompany drugs governed by the Food and Drugs Administration (FDA), hold information about Adverse Drug Reactions (ADRs) that exists associated with drugs post-market. This information is valuable for actors working in the field of pharmacovigilance aiming to improve the safety of drugs. One such actor is Uppsala Monitoring Centre (UMC), a non-profit conducting pharmacovigilance research. In order to access the valuable information of the package inserts, UMC have constructed an SPL mining pipeline in order to mine SPLs for ADRs. This project aims to investigate new approaches to the solution to the Scan problem, the part of the pipeline responsible for extracting mentions of ADRs. The Scan problem is solved by approaching the problem as a Named Entity Recognition task, a subtask of Natural Language Processing. By using the transformer-based deep learning model BERT, with domain-specific pre-training, an F1-score of 0.8220 was achieved. Furthermore, the chosen model was used in an iteration of Active Learning in order to efficiently extend the available data pool with the most informative examples. Active Learning improved the F1-score to 0.8337. However, the Active Learning was benchmarked against a data set extended with random examples, showing similar improved scores, therefore this application of Active Learning could not be determined to be effective in this project.