Relation Classification Between the Extracted Entities of Swedish Verdicts

Sammanfattning: This master thesis investigated how well a multiclass support vector machine approach is at classifying a fixed number of interpersonal relations between extracted entities of people from Swedish verdicts. With the help of manually tagged extracted pairs of people entities called relations, a multiclass support vector machine was used to train and test the performance of the classification. Different features and parameters were tested to optimize the method, and for the final experiment, a micro precision and recall of 91.75% were found. For macro precision and recall, the result was 73.29% and 69.29% respectively. This resulted in an macro F score of 71.23% and micro F score of 91.75%. The results showed that the method worked for a few of the relation classes, but more balanced data would have been needed to answer the research question to a full extent.