CLASSIFYING TERMS FROM SOURCE TEXTS USING IMPLICIT AND EXPLICIT CLASS-RECOGNITION-MACHINE-LEARNING MODELS

    公开(公告)号:US20210027141A1

    公开(公告)日:2021-01-28

    申请号:US16518894

    申请日:2019-07-22

    Applicant: Adobe Inc.

    Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can classify term sequences within a source text based on textual features analyzed by both an implicit-class-recognition model and an explicit-class-recognition model. For example, by applying machine-learning models for both implicit and explicit class recognition, the disclosed systems can determine a class corresponding to a particular term sequence within a source text and identify the particular term sequence reflecting the class. The dual-model architecture can equip the disclosed systems to apply (i) the implicit-class-recognition model to recognize implicit references to a class in source texts and (ii) the explicit-class-recognition model to recognize explicit references to the same class in source texts.

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