This paper describes our approach for the Community Question Answering Task, which was presented at the SemEval 2015. The system should read a given question and identify good, potentially relevant, and bad answers for that question. Our approach transforms the answers of the training set into a graph based representation for each answer class, which contains lexical, morphological, and syntactic features. The answers in the test set are also transformed into the graph based representation individually. After this, different paths are traversed in the training and test sets in order to find relevant features of the graphs. As a result of this procedure, the system constructs several vectors of features: one for each traversed graph. Finally, a cosine similarity is calculated between the vectors in order to find the class that best matches a given answer. Our system was developed for the English language only, and it obtained an accuracy of 53.74 for subtask A and 44.0 for subtask B.
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