Abstract: A computer-implemented method of tagging a text, comprises: determining a value for each of a plurality of locations in a first vector; processing (402), by a trained first neural network component, the first vector to generate a second vector; processing (404), at a trained second neural network component, the second vector to generate a probability score for each of at least ten predetermined tags; determining (406) if each probability score meets a criterion; if the criterion is met, assigning (408) the tag corresponding to the probability score to the text. Each of the locations may correspond to a respective predetermined word, each value relating to existence and/or frequency of the corresponding word in the text, and the number of locations may be between 600 and 20000. The number of locations in the second vector may be fewer than the number of locations in the first vector and is from 100 to 5000.

Abstract: A computer-implemented method of tagging a text, comprises: determining a value for each of a plurality of locations in a first vector; processing (402), by a trained first neural network component, the first vector to generate a second vector; processing (404), at a trained second neural network component, the second vector to generate a probability score for each of at least ten predetermined tags; determining (406) if each probability score meets a criterion; if the criterion is met, assigning (408) the tag corresponding to the probability score to the text. Each of the locations may correspond to a respective predetermined word, each value relating to existence and/or frequency of the corresponding word in the text, and the number of locations may be between 600 and 20000.

Abstract: This is provided a method of labelling a message or group of messages. An input is received (208) at a neural network (300, 302) including at least one recurrent layer, which may comprises LSTM memory blocks (300). The input comprising at least one word vector (xt), which represents at least one word in a message, and the at least one word vector defines a meaningful position in a word vector space. Typically the input is a sequence of word vectors corresponding to a sequence of words. The input is then processed to generate a plurality of network outputs. Each network output corresponds to a respective one of a plurality of labels. Based on the network outputs, a probability score for each of the labels is then generated (210). If it is determined (212) that at least one of the probability scores meets at least one criterion, the at least one label corresponding to the at least one probability score for which the at least one criterion is met is assigned (214) to the message.