This paper presents two methods for self-adapting the semantic sensitivities in a recently proposed semantics-based crossover: Semantic Similarity based Crossover (SSC). The first self-adaptation method is inspired by a self-adaptive method for controlling mutation step size in Evolutionary Strategies (1/5 rule). The design of the second takes into account more of our previous experimental observations, that SSC works well only when a certain portion of events successfully exchange semantically similar subtrees. These two proposed methods are then tested on a number of real-valued symbolic regression problems, their performance being compared with SSC using predetermined sensitivities and with standard crossover. The results confirm the benefits of the second self-adaption method.