Abstract: This document relates to alignment of time-varying signals, such as sound. One example includes a method or technique that can be performed on a computing device. The method or technique can include inputting a first time-varying signal and a second time-varying signal into a trained model. The method or technique can also include performing temporal alignment of the first time-varying signal to the second time-varying signal using an attention layer of the trained model to obtain a temporally-aligned first time-varying signal. The method or technique can also include enhancing the second time-varying signal based at least on the temporally-aligned first time-varying signal, and outputting the enhanced second time-varying signal.

Abstract: Systems and methods for information extraction from natural language texts using a combination of classifiers analyzing local and non-local features. An example method may comprise: extracting, by a computer system, a plurality of features associated with each text segment of a plurality of text segments of a natural language text; associating one or more tags with each text segment of the plurality of text segments by processing, using a stage one classifier, the extracted features associated with the text segment; extracting, from a local context of a candidate token of a text segment of the plurality of text segments, a plurality of local features associated with the candidate token; and processing, by a stage two classifier, a combination of the plurality of local features and the tags associated with the text segment to determine a degree of association of an information object referenced by the candidate token with a category of information objects.

Abstract: Systems and methods for marking a natural language text to identify segments of different types. The method includes using a classification model to classify candidate segments as belonging to one of the types where the classifiers of the training model are trained on a marked natural language text.

Abstract: Systems and methods for reconstructing textual annotations associated with information objects. An example method comprises: receiving a natural language text associated with a plurality of information objects, wherein each information object is associated with one or more attributes; identifying an information object of the plurality of information objects, such that at least one attribute of the identified information object is not associated with at least one textual annotation; identifying one or more candidate textual annotations to be associated with the attribute, such that each candidate textual annotation is represented by a fragment of the natural language text referencing the value of the attribute; determining ranking scores of the identified candidate textual annotations; and selecting one or more candidate textual annotations having an optimal ranking score.