Abstract: There is disclosed a method of and a system for generating a database query. A natural language input for requesting data from a database is received. A first portion of the natural language input is input to a first machine learning algorithm (MLA). A second portion of the natural language input is input to a second MLA. The first MLA outputs a first probability distribution. The second MLA outputs a second probability distribution. One or more sets of classes from the first probability distribution and the second probability distribution that satisfy a plurality of constraints are determined. A predicted probability is determined for each of the one or more sets of classes. A set of classes having a highest predicted probability from the one or more sets of classes is selected. A database query is generated based on the set of classes.

Abstract: There is disclosed a method of and a system for generating a database query. A natural language input for requesting data from a database is received. A first portion of the natural language input is input to a first machine learning algorithm (MLA). A second portion of the natural language input is input to a second MLA. The first MLA outputs a first probability distribution. The second MLA outputs a second probability distribution. One or more sets of classes from the first probability distribution and the second probability distribution that satisfy a plurality of constraints are determined. A predicted probability is determined for each of the one or more sets of classes. A set of classes having a highest predicted probability from the one or more sets of classes is selected. A database query is generated based on the set of classes.

Abstract: There is disclosed a method of and a system for generating a database query. A natural language input for requesting data from a database is received. A first portion of the natural language input is input to a first machine learning algorithm (MLA). A second portion of the natural language input is input to a second MLA. The first MLA outputs a first probability distribution. The second MLA outputs a second probability distribution. One or more sets of classes from the first probability distribution and the second probability distribution that satisfy a plurality of constraints are determined. A predicted probability is determined for each of the one or more sets of classes. A set of classes having a highest predicted probability from the one or more sets of classes is selected. A database query is generated based on the set of classes.

Abstract: There is disclosed a method of and a system for predicting text in an image using one or more constraints. The image is input to a machine learning algorithm (MLA). The MLA outputs a probability distribution. The probability distribution comprises a predicted probability for each of a plurality of pairs, where each pair comprises a class and a next state of the MLA. The states of the probability distribution are added to a set of states to be searched. States that are end states or that fail to satisfy at least one of the constraints are removed from the set of states to be searched. States of the set of states to be searched are input to the MLA. The search is repeated with new states output by the MLA. End states output by the MLA are output as output states that each comprise a sequence of characters.

Abstract: There is disclosed a method and system for determining states satisfying one or more constraints. The method comprises inputting, to a machine learning algorithm (MLA), input data. The MLA outputs a probability distribution. The probability distribution comprises a predicted probability for each of a plurality of pairs, where each pair comprises a class in a set of classes and a corresponding state of the MLA. The states of the probability distribution are added to a set of states to be searched. States that are end states or that fail to satisfy at least one of the constraints are removed from the set of states to be searched. One or more of the set of states to be searched are input to the MLA. The search is repeated with new states output by the MLA. End states output by the MLA may be output as output states.

Abstract: There is disclosed a method of and a system for predicting text in an image using one or more constraints. The image is input to a machine learning algorithm (MLA). The MLA outputs a probability distribution. The probability distribution comprises a predicted probability for each of a plurality of pairs, where each pair comprises a class and a next state of the MLA. The states of the probability distribution are added to a set of states to be searched. States that are end states or that fail to satisfy at least one of the constraints are removed from the set of states to be searched. States of the set of states to be searched are input to the MLA. The search is repeated with new states output by the MLA. End states output by the MLA are output as output states that each comprise a sequence of characters.