Utjecaj digitalnih resursa i alata na kvalitetu prijevoda u računalno potpomognutom prevođenju

Ovaj diplomski rad proučava utjecaj digitalnih resursa i alata na kvalitetu prijevoda u računalno potpomognutom prevođenju (engl. Computer-Aided Translation – CAT). U radu je prikazan pregled najvažnijih definicija iz spomenutog područja, a kvaliteta je analizirana u okviru procesa osiguranja kvalitete prijevoda (engl. Quality Assurance – QA). Sama provjera kvalitete je provedena na dvojezičnom englesko-hrvatskom tekstu, dobivenom postupkom sravnjivanja korpusa. U radu je korišteno nekoliko alata za računalno potpomognuto prevođenje u više različitih zadataka: sravnjivanju dokumenata, ekstrakciji terminologije te analizi kvalitete prijevoda. Njihova će usporedba pokazati koliko se kvaliteta prijevoda pospješuje korištenjem svih mogućnosti koje ti alati nude.This master's thesis analyses the impact of digital resources and tools on translation quality in computer-aided translation (CAT). Thesis presents the most important definitions from the mentioned area, and quality is analysed through the quality assurance process (QA). The quality is checked on bilingual english-croatian texts, obtained by alignment of corpus of texts written in those languages. Several CAT tools are used in different tasks: alignment, terminology extraction and analyse of translation quality. Their comparison will show how much the translation quality increases by using all the features they offer

An ontology-based model for risk management in mining

Rudarska proizvodnja obuhvata kompleksne tehnološke sisteme, što nameće potrebu za uspostavljanjem i unapređivanjem sistema upravljanja rizikom. Heterogenost i obim podataka neophodnih za upravljanje rizikom zahtevaju sistem koji ih na fleksibilan način integriše i omogućava njihovo optimalno korišćenje. Osnovni cilj ove disertacije je razvoj ontologije za domen rudarstva i na njoj zasnovanog modela za upravljanje rizikom. Njegova realizacija podrazumeva i implementaciju algoritama ekstrakcije informacija za popunjavanje ontologije, kao i odgovarajuće softversko rešenje. Razvoj modela obuhvata i značajno proširenje rudarskog korpusa, kao i kreiranje terminološke baze podataka, realizovano korišćenjem metoda računarske lingvistike i korpusa dokumenata iz oblasti rudarstva (planova, izveštaja, zakona, udžbenika i monografija). Korišćena je i deskriptivna metoda za sistematizaciju podataka, zatim konačni automati i statističke analize za ekstrakciju informacija, kao i komparativna i analitička istraživačka metoda za vrednovanje i interpretaciju dobijenih rezultata. Za razvoj modela korišćeni su alati informacionih tehnologija: UML za modeliranje koncepata , OWL za razvoj ontologije, SWRL pravila za mehanizam zaključivanja, upitni jezici CQL nad korpusom i SPARQL nad ontologijom . Rezultati istraživanja pokazuju da je moguće formalizovati informacije i znanje o rizicima u rudarstvu, te razviti model koji će unaprediti efikasnost upravljanja rizikom i pomoći menadžmentu rudnika u donošenju odluka o primeni mera za smanjenje uticaja rizika identifikovanih u rudniku. Ostvarenjem ciljeva ove disertacije dat je doprinos povećanju efikasnosti u identifikaciji, analizi i reagovanju na rizik kroz izgradnju specifične domenske ontologije za rizike u rudarstvu.Mining production involves complex technological systems, which calls for the need to create and improve risk management systems. The heterogeneity and scope of data necessary for risk management require a system that integrates them in a flexible way and enables their optimal use. The main goal of this dissertation is to develop an ontology for the mining domain and a risk management model based on it. Its realization includes the implementation of information extraction algorithms for improving the ontology, as well as an appropriate software solution. The development of the model includes a significant expansion of the mining corpus, as well as the creation of a terminological database, realized using methods of computational linguistics and a corpus of documents from the mining domain (plans, reports, laws, textbooks and monographs). For systematization of data a descriptive method was used, finite automata and statistical analyzes for information extraction, and comparative and analytical research methods for evaluation and interpretation of the obtained results. Information technology tools were used for model development: UML for concept modeling, OWL for ontology development, SWRL rules for inference mechanism, query languages CQL for corpus and SPARQL for ontology. The research results show that it is possible to formalize information and knowledge about risks in mining and develop a model that will improve the efficiency of risk management and assist mine management in making decisions on implementing measures to reduce the impact of risks identified in a mine. Achieving the goals of this dissertation has contributed to increasing efficiency in identification, analysis and response to risk by developing a specific domain ontology for risks in mining

Uticaj klasifikacije teksta na primene u obradi prirodnih jezika

The main goal of this dissertation is to put different text classification tasks in the same frame, by mapping the input data into the common vector space of linguistic attributes. Subsequently, several classification problems of great importance for natural language processing are solved by applying the appropriate classification algorithms. The dissertation deals with the problem of validation of bilingual translation pairs, so that the final goal is to construct a classifier which provides a substitute for human evaluation and which decides whether the pair is a proper translation between the appropriate languages by means of applying a variety of linguistic information and methods. In dictionaries it is useful to have a sentence that demonstrates use for a particular dictionary entry. This task is called the classification of good dictionary examples. In this thesis, a method is developed which automatically estimates whether an example is good or bad for a specific dictionary entry. Two cases of short message classification are also discussed in this dissertation. In the first case, classes are the authors of the messages, and the task is to assign each message to its author from that fixed set. This task is called authorship identification. The other observed classification of short messages is called opinion mining, or sentiment analysis. Starting from the assumption that a short message carries a positive or negative attitude about a thing, or is purely informative, classes can be: positive, negative and neutral. These tasks are of great importance in the field of natural language processing and the proposed solutions are language-independent, based on machine learning methods: support vector machines, decision trees and gradient boosting. For all of these tasks, a demonstration of the effectiveness of the proposed methods is shown on for the Serbian language.Osnovni cilj disertacije je stavljanje različitih zadataka klasifikacije teksta u isti okvir, preslikavanjem ulaznih podataka u isti vektorski prostor lingvističkih atributa..