Occurrences of named entities (i. e. people, organizations, locations, etc.) often appear within text in form of relations, such as acquaintance, influence, collaboration, and various others. Named entity relation extraction is a task of great importance, espe- cially considering the sheer scale of presently availabe text written in natural language. Typical implementations are based on supervised learning algorithms, classifying each pair of named entities individually, in order to determine the relation between the two. However, such approaches do not take into account the structural nature of relations in the form of intra-relational influences and constraints at training time, sometimes com- pensating for the resulting drawbacks by post-processing outputs via global loss opti- mization. Recent research in search-based structured prediction opens new venues for attempting to solve relation extraction. This thesis focuses of a search-based structured prediction algorithm, SEARN, adapted to the task of named entity relation extraction in Croatian language. We introduce a novel dataset for relation extraction in Croatian. We then proceed by describing and implementing a rule-based predictor, a configura- tion of support vector machines, and a SEARN-based model for named entity relation extraction. The models are then compared on the produced dataset, their performance being evaluated in terms of F1 scores and related metrics