<p>With the development of the Internet, illegal copying of electronic documents has become a growing problem. There is an increasing need of prevention in the field of pirate copying. One method is to mark the document by changing some of the words to synonyms. In this way it is possible to construct legal copies which do not differ in content but still are unique. Since the copies of the documents are unique, it is possible to trace the owner of a document and accordingly call him or her to account for pirate copying if several exactly similar copies are reaching the market. </p><p>The aim of this study was to investigate the possibility of exchanging words for synonyms in a text from a work of fiction, examining both the literary qualities of the manipulated texts and the security aspect. The conclusion of the study is that it is possible to mark texts of imaginative literature by means of the use of synonyms.</p>

With the development of the Internet, illegal copying of electronic documents has become a growing problem. There is an increasing need of prevention in the field of pirate copying. One method is to mark the document by changing some of the words to synonyms. In this way it is possible to construct legal copies which do not differ in content but still are unique. Since the copies of the documents are unique, it is possible to trace the owner of a document and accordingly call him or her to account for pirate copying if several exactly similar copies are reaching the market. The aim of this study was to investigate the possibility of exchanging words for synonyms in a text from a work of fiction, examining both the literary qualities of the manipulated texts and the security aspect. The conclusion of the study is that it is possible to mark texts of imaginative literature by means of the use of synonyms.

Att utveckla en sofistikerad chatbotlösning kräver stora mängder textdata för att kunna anpassalösningen till en specifik domän. Att manuellt skapa en komplett uppsättning textdata, specialanpassat för den givna domänen och innehållandes ett stort antal varierande meningar som en människa kan tänkas yttra, är ett enormt tidskrävande arbete. För att kringgå detta tillämpas dataaugmentering för att generera mer data utifrån en mindre uppsättning redan existerande textdata. Softronic AB vill undersöka alternativa strategier för dataaugmentering med målet att eventuellt ersätta den nuvarande lösningen med en mer vetenskapligt underbyggd sådan. I detta examensarbete har prototypmodeller utvecklats för att jämföra och utvärdera effekten av olika textaugmenteringsstrategier. Resultatet av genomförda experiment med prototypmodellerna visar att augmentering genom synonymutbyten med en domänanpassad synonymordlista, presenterade märkbart förbättrade effekter på förmågan hos en NLU-modell att korrekt klassificera data, gentemot övriga utvärderade strategier. Vidare indikerar resultatet att ett samband föreligger mellan den strukturella variationsgraden av det augmenterade datat och de tillämpade språkparens semantiska likhetsgrad under tillbakaöversättningar. / Developing a sophisticated chatbot solution requires large amounts of text data to be able to adapt the solution to a specific domain. Manually creating a complete set of text data, specially adapted for the given domain, and containing a large number of varying sentences that a human conceivably can express, is an exceptionally time-consuming task. To circumvent this, data augmentation is applied to generate more data based on a smaller set of already existing text data. Softronic AB wants to investigate alternative strategies for data augmentation with the aim of possibly replacing the current solution with a more scientifically substantiated one. In this thesis, prototype models have been developed to compare and evaluate the effect of different text augmentation strategies. The results of conducted experiments with the prototype models show that augmentation through synonym swaps with a domain-adapted thesaurus, presented noticeably improved effects on the ability of an NLU-model to correctly classify data, compared to other evaluated strategies. Furthermore, the result indicates that there is a relationship between the structural degree of variation of the augmented data and the applied language pair's semantic degree of similarity during back-translations.