Systems and methods for performing contextual classification using supervised and unsupervised training

摘要

Computerized systems and methods are disclosed for performing contextual classification of objects using supervised and unsupervised training. In accordance with one implementation, content reviewers may review training objects and submit supervised training data for preprocessing and analysis. The supervised training data may be preprocessed to identify key terms and phrases, such as by stemming, tokenization, or n-gram analysis, and form vectorized objects. The vectorized objects may be used to train one or more models for subsequent classification of objects. In certain implementations, preprocessing or training, among other steps, may be performed in parallel over multiple machines to improve efficiency. The disclosed systems and methods may be used in a wide variety of applications, such as article classification and content moderation.

主权项

1. A computer-implemented method for performing contextual classification of objects, comprising:
receiving supervised training data transmitted over a network from at least one content reviewer, the supervised training data comprising at least one comment and at least one tag specifying whether the at least one comment is abusive; storing the supervised training data in a database; preprocessing, with at least one processor, the supervised training data to form at least one vectorized object; training a plurality of models by applying a plurality of machine learning algorithms to each vectorized object in parallel; identifying, with at least one processor, an optimal model from the plurality of models; and filtering at least one abusive comment using the identified optimal model.