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This paper describes the SoccEval Annotation Project, an annotation schema designed to support machine-learning classification efforts to evaluate the performance of soccer players based on match reports taken from online news sources. In addition to factual information about player attributes and actions, the schema annotates subjective opinions about them. After explaining the annotation schema and annotation process, we describe a machine learning experiment. Classifiers trained on features derived from annotated data performed better than a baseline trained on unigram features. Initial results suggest that improvements can be made to the annotation scheme and guidelines as well as the amount of data annotated. We believe our schema could be potentially expanded to extract more information about soccer players and teams.

SoccEval: An Annotation Schema for Rating Soccer Players