An instance fed into the first network consists of a list of tuples, each comprising the span of the token, encoded by a word embedding, as well as a left context and a right context. Our previous work determined a context of the previous two and the next two tokens (also encoded by a pre-trained word embedding) as best performing for the determination of character references. The last input vector was derived by a Bi-LSTM character encoding of the target word. The tuples were arranged in order of appearance in the original text and encoded by a Bi-LSTM, feeding an additional tupel at each time step. The Bi-LSTM subsequently generates a condensed representation of those tuples into a vector of 256 units. The intuition is that this vector contains the most informative parts of all contexts for a given target word. The network is trained using log-loss and predicts whether the target word is a reference or not.