Abstract: A method for performing hierarchical classification of data is disclosed. The method is being executed by at least one processing device. The method includes receiving input data for encoding into multiple channels. Further, the method includes extracting one or more features and one or more temporal structures corresponding to the input data. The method further includes identifying one or more feature dependences in the input data. Further, the method includes combining the extracted one or more features corresponding to the input data, the extracted one or more temporal structures of the input data, and the identified one or more feature dependencies in the input data into a combined feature set. Thereafter, the method includes classifying the combined feature set into one or more output classes, and thereby performing the hierarchical classification of data.

Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.

Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.

Abstract: A microprocessor executable method transforms unstructured natural language texts by way of a preprocessing pipeline into a structured data representation of the entities described in the original text. The structured data representation is conducive to further processing by machine methods. The transformation process is learned by a machine learned model trained to identify relevant text segments and disregard irrelevant text segments The resulting structured data representation is refined to more accurately represent the respective entities.

Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.