  We provide a learning-based technique for guessing a winning strategy in a
parity game originating from an LTL synthesis problem. A cheaply obtained guess
can be useful in several applications. Not only can the guessed strategy be
applied as best-effort in cases where the game's huge size prohibits rigorous
approaches, but it can also increase the scalability of rigorous LTL synthesis
in several ways. Firstly, checking whether a guessed strategy is winning is
easier than constructing one. Secondly, even if the guess is wrong in some
places, it can be fixed by strategy iteration faster than constructing one from
scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize
exploration in the most fruitful directions.
  In contrast to previous works, we (i)~reflect the highly structured logical
information in game's states, the so-called semantic labelling, coming from the
recent LTL-to-automata translations, and (ii)~learn to reflect it properly by
learning from previously solved games, bringing the solving process closer to
human-like reasoning.
