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.

Text has been the historical way of preserving and acquiring knowledge, and text data today is an increasingly growing part of the digital footprint together with the need to query this data for information. Seeking information is a constant ongoing process, and is a crucial part of many systems all around us. The ability to perform fast and effective searches is a must when dealing with vast amounts of data. This thesis implements an information retrieval system based on the Swedish Defence Force's profession guide, with the aim to produce a system that retrieves relevant professions based on user defined queries of varying size. A number of Natural Language Processing techniques are investigated and implemented, in order to transform the gathered profession descriptions a document embedding model, doc2vec, was implemented resulting in document vectors that are compared to find similarities between documents. The final system was evaluated by domain experts, represented by active military personal that quantified the relevancy of the profession retrievals into a measurable performance. The system managed to retrieve relevant information for 46.6% and 56.6% of the long- and short text inputs respectively. Resulting in a much more generalized and capable system compared to the search function available at the profession guide today.