The vast majority of text freely available on the Internet is not available in a form that computers can understand. There have been numerous approaches to automatically extract information from human- readable sources. The most successful attempts rely on vast training sets of data. Others have succeeded in extracting restricted subsets of the available information. These approaches have limited use and require domain knowledge to be coded into the application. The current thesis proposes a novel framework for Information Extraction. From large sets of documents, the system develops statistical models of the data the user wishes to query which generally avoid the lim- itations and complexity of most Information Extractions systems. The framework uses a semi-supervised approach to minimize human input. It also eliminates the need for external Named Entity Recognition systems by relying on freely available databases. The final result is a query-answering system which extracts information from large corpora with a high degree of accuracy.

Sport clubs are always searching for innovative ways to improve performance and obtain a competitive edge. Sports analytics today is focused primarily on evaluating metrics thought to be directly tied to performance. Injuries indirectly decrease performance and cost substantially in terms of wasted salaries. Existing sports injury research mainly focuses on correlating one specific feature at a time to the risk of injury. This paper provides a multidimensional approach to non-contact injury prediction in Swedish professional ice hockey by applying machine learning on historical data. Several features are correlated simultaneously to injury probability. The project’s aim is to create an injury predicting algorithm which ranks the different features based on how they affect the risk of injury. The paper also discusses the business potential and strategy of a start-up aiming to provide a solution for predicting injury risk through statistical analysis. / Idrottsklubbar letar ständigt efter innovativa sätt att förbättra prestation och erhålla konkurrensfördelar. Idag fokuserar data- analys inom idrott främst på att utvärdera mätvärden som tros vara direkt korrelerade med prestation. Skador sänker indirekt prestationen och kostar markant i bortslösade spelarlöner. Tidigare studier på skador inom idrotten fokuserar huvudsakligen på att korrelera ett mätvärde till en skada i taget. Den här rapporten ger ett multidimensionellt angreppssätt till att förutse skador inom svensk elitishockey genom att applicera maskininlärning på historisk data. Flera attribut korreleras samtidigt för att få fram en skadesannolikhet. Målet med den här rapporten är att skapa en algoritm för att förutse skador och även ranka olika attribut baserat på hur de påverkar skaderisken. I rapporten diskuteras även affärsmöjligheterna för en sådan lösning och hur en potentiell start-up ska positionera sig på marknaden.