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The purpose of this paper is to describe our submission to the SemEval-2015 Task 3 on Answer Selection in Community Question Answering. We participated in subtask A, where the systems had to classify community answers for a given question as definitely relevant, potentially useful, or irrelevant. For every question-answer pair in the training data we extract a vector with a variety of features. These vectors are then fed to a MaxEnt classifier for training. Given a question and an answer the trained classifier outputs class probabilities for each of the three desired categories. The one with the highest probability is chosen. Our system scores better than the average score in subtask A of Task 3.

Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features