How natural language processing can be used to improve digital language learning

Sammanfattning: The world is facing globalization and with that, companies are growing and need to hire according their needs. A great obstacle for this is the language barrier between job applicants and employers who want to hire competent candidates. One spark of light in this challenge is Lingio, who provides a product that teaches digital profession-specific Swedish. Lingio intends to make their existing product more interactive and this research paper aims to research aspects involved in that. This study evaluates system utterances that are planned to be used in Lingio’s product for language learners to use in their practice and studies the feasibility of using the natural language model cosine similarity in classifying the correctness of answers to these utterances. This report also looks at whether it best to use crowd sourced material or a golden standard as benchmark for a correct answer. The results indicate that there are a number of improvements and developments that need to be made to the model in order for it to accurately classify answers due to its formulation and the complexity of human language. It is also concluded that the utterances by Lingio might need to be further developed in order to be efficient in their use for learning language and that crowd sourced material works better than a golden standard. The study makes several interesting observations from the collected data and analysis, aiming to contribute to further research in natural language engineering when it comes to text classification and digital language learning.