Extracting Formal Models from Normative Texts

We are concerned with the analysis of normative texts - documents based on the deontic notions of obligation, permission, and prohibition. Our goal is to make queries about these notions and verify that a text satisfies certain properties concerning causality of actions and timing constraints. This requires taking the original text and building a representation (model) of it in a formal language, in our case the C-O Diagram formalism. We present an experimental, semi-automatic aid that helps to bridge the gap between a normative text in natural language and its C-O Diagram representation. Our approach consists of using dependency structures obtained from the state-of-the-art Stanford Parser, and applying our own rules and heuristics in order to extract the relevant components. The result is a tabular data structure where each sentence is split into suitable fields, which can then be converted into a C-O Diagram. The process is not fully automatic however, and some post-editing is generally required of the user. We apply our tool and perform experiments on documents from different domains, and report an initial evaluation of the accuracy and feasibility of our approach.Comment: Extended version of conference paper at the 21st International Conference on Applications of Natural Language to Information Systems (NLDB 2016). arXiv admin note: substantial text overlap with arXiv:1607.0148

Dependency-Based Hybrid Model of Syntactic Analysis for the Languages with a Rather Free Word Order

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 13-20

COVID-19 News and Audience Aggressiveness: Analysis of News Content and Audience Reaction During the State of Emergency in Latvia (2020–2021)

Funding Information: ACKNOWLEDGEMENTS This study was supported by the Ministry of Education and Science, Republic of Latvia, as part of the project “Life with COVID-19: Evaluation of overcoming the coronavirus crisis in Latvia and recommendations for societal resilience in the future” [grant number VPP-COVID-2020/1-0013]. Publisher Copyright: © 2021 Anda Rozukalne, Vineta Kleinberga, Normunds Grūzītis. Published by Rezekne Academy of Technologies.This research focuses on the interrelation between news content on COVID-19 of three largest online news sites in Latvia (delfi.lv, apollo.lv, tvnet.lv) and the audience reaction to the news in the Latvian and Russian channels during the state of emergency. By using a tool for audience behaviour analysis, the Index of the Internet Aggressiveness (IIA), for analysis of audience comments, the study aims to uncover how and whether news about COVID-19 affect the level of audience aggressiveness. The study employs two data collection methods: news content analysis and IIA data analysis, in which ten index peaks are selected in each of the two emergency periods (spring 2020, fall and winter 2020/21). The study data consists of content analysis of 400 news items and analysis of ~80,000 comments, identifying the level of aggressiveness, the number and structure of comment keywords. The results show that the level of public aggressiveness is only partially formed by the attitude towards COVID-19 news: less than half of the most aggressively commented news is devoted to information about COVID-19. An increase in the level of aggressiveness of the audience of online news sites can be observed at the end of 2020 and at the beginning of 2021 when it is higher than over the course of 2020. IIA is an online comment analysis platform, which analyses user-generated comments on news on online news sites according to pre-selected keywords, allowing to grasp the dynamics of commenters’ verbal aggressiveness. In addition, IIA exploits a machine learned classifier to recognize not only potentially aggressive keywords but also to analyse the entire comments. In January 2021, the IIA data set consists of ~24.89 million comments (~611.97 million words) added to ~1.34 million news articles.publishersversionPeer reviewe

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr

Formal grammar and semantics of controlled Latvian language

Elektroniskā versija nesatur pielikumusPromocijas darba pētījuma priekšmets ir latviešu valodas kā izteikti fleksīvas, sintētiskas valodas automātiska gramatiskā un semantiskā analīze. Darbā ir piedāvāts oriģināls, hibrīds gramatikas modelis, kas ir piemērots tādu valodu sintaktiskajai analīzei, kurās vārdu secība ir relatīvi brīva. Izstrādātais modelis ir aprobēts praksē, formalizējot latviešu valodas apakškopu, kas aptver dažādas vienkāršos paplašinātos teikumos sastopamas sintaktiskās konstrukcijas. Tālāk problēma tiek sašaurināta ne tikai sintaktiski, bet arī semantiski, izstrādājot viennozīmīgu, taču iespējami dabisku ierobežotu latviešu valodu (un atbilstošus automātiskas analīzes/sintēzes līdzekļus), kuras semantika ir definēta aprakstošajā loģikā. Darbā ir parādīts, ka teikuma informatīvās struktūras analīze ir pietiekams līdzeklis viennozīmīgai kvantoru un koreferenču noteikšanai ierobežotas sintētiskas valodas formā dotās OWL terminoloģiskajās aksiomās, SWRL izvedumu likumos un SPARQL integritātes vaicājumos. Darbā ir piedāvāta un realizēta divlīmeņu tulkošanas pieeja, demonstrējot ierobežotās latviešu valodas teikumu automātisku, semantiski precīzu translēšanu uz OWL (un otrādi), lietojot esošu ierobežotu angļu valodu kā starpvalodu un atkalizmantojot tās rīkus. Papildus ir piedāvāta pusautomātiska metode sistemātiskas leksiskās daudznozīmības atbalstam un nozīmju precīzai izšķiršanai ierobežotas valodas tekstos, vienlaikus risinot OWL ontoloģiju sastatīšanas problēmu.The research subject of this doctoral thesis is the formal, automatic grammatical and semantic analysis of the highly inflective, synthetic Latvian language. A novel hybrid grammar model is proposed, which is especially suited for languages with relatively free word order. The model has been tested on a syntactically restricted subset of Latvian, covering various constructions that can be found in simple extended sentences. The problem is then restricted also from the semantic perspective by developing a deterministic, yet natural subset of Latvian (accompanied with its parser and generator), whose semantics is defined in description logic. The author shows that the analysis of the information structure of a sentence is a reliable way to unambiguously identify the implicit quantifiers and coreferences in OWL terminological axioms, SWRL inference rules and SPARQL integrity queries that are given in a form of a controlled synthetic language. A two-level translation approach is proposed and implemented in a prototype that demonstrates the semantically precise machine translation from controlled Latvian to OWL (and vice versa) by using an existing controlled English as an interlingua and by reusing its readily available tools. In addition, a semi-automatic method is proposed to enable controlled, systematic polysemy and word sense disambiguation in controlled language texts, simultaneously dealing with the OWL ontology merging problem