Abstract: There are provided methods and systems for multi-label classification of a sentence. An example method includes obtaining the sentence and generating a first digital representation corresponding to the words of the sentence. The method also includes performing a first classification of the sentence using a classification engine receiving as input the first digital representation. The first classification generates a first set of probabilities each associated with one of the possible labels for the sentence. The classification engine may include a neural network. The method further includes generating an output probability for each given label of the possible labels, which output probability is generated based on a first probability associated with the given label. The first probability is from the first set of probabilities. Moreover, the method includes outputting the output probability for each of the possible labels.

Abstract: There are provided methods and systems for multi-label classification of a sentence. An example method includes obtaining the sentence and generating a first digital representation corresponding to the words of the sentence. Th method also includes performing a first classification of the sentence using a classification engine receiving as input the first digital representation. The first classification generates a first set of probabilities each associated with one of the possible labels for the sentence. The classification engine may include a neural network. The method further includes generating an output probability for each given label of the possible labels, which output probability is generated based on a first probability associated with the given label. The first probability is from the first set of probabilities. Moreover, the method includes outputting the output probability for each of the possible labels.