Abstract: Techniques for training and using machine learning models for resource-level classification. A method for training includes refining outputs of a language model by providing a prompt and a set of sample resources to the language model over a series of iterations. Accuracies for the classifications output by the language model at each iteration are determined based on semantic similarity between those classifications and corresponding reference classifications for the sample resources. The language model is applied to data of a set of training resources when the outputs of the language model have been refined, in order to output a set of classifications for the set of training resources. Training data is labeled based on the set of classifications output by the language model. A classifier machine learning model is trained via supervised machine learning using the set of labeled training data in order to produce a trained classifier machine learning model.

Abstract: A system and method for classification. A method includes identifying candidate entities among text data by applying at least one entity identification rule to the text data. Inputs are constructed based on the identified candidate entities, where each input includes a first portion of text indicating a candidate entity and at least one second portion of text and where the at least one second portion of text of each input is adjacent to the first portion of text of the input. Multiple language models are applied to the inputs, where each language model is trained to identify a respective set of entities and where outputs of the language models include at least one portion of entity-indicating text for each input. Based on the outputs of the language models, at least one named entity in the text data is determined.

Abstract: A system and method for classification. A method includes identifying candidate entities among text data by applying at least one entity identification rule to the text data. Inputs are constructed based on the identified candidate entities, where each input includes a first portion of text indicating a candidate entity and at least one second portion of text and where the at least one second portion of text of each input is adjacent to the first portion of text of the input. Multiple language models are applied to the inputs, where each language model is trained to identify a respective set of entities and where outputs of the language models include at least one portion of entity-indicating text for each input. Based on the outputs of the language models, at least one named entity in the text data is determined.

Abstract: Techniques for data classification using clustering. A method includes replacing a plurality of portions of metadata for a plurality of data objects with a plurality of replacement characters in order to generate a plurality of replaced strings; clustering the plurality of data objects into a plurality of clusters based on commonalities between the plurality of replaced strings of data objects of the plurality of data objects; classifying a subset of the data objects in each cluster into at least one class; and aggregating classes within at least one cluster of the plurality of clusters, wherein aggregating classes within each of the at least one cluster includes applying the at least one class for the subset of the data objects in each cluster to each other data object within the cluster.

Abstract: Techniques for training and using machine learning models for resource-level classification. A method for training includes refining outputs of a language model by providing a prompt and a set of sample resources to the language model over a series of iterations. Accuracies for the classifications output by the language model at each iteration are determined based on semantic similarity between those classifications and corresponding reference classifications for the sample resources. The language model is applied to data of a set of training resources when the outputs of the language model have been refined, in order to output a set of classifications for the set of training resources. Training data is labeled based on the set of classifications output by the language model. A classifier machine learning model is trained via supervised machine learning using the set of labeled training data in order to produce a trained classifier machine learning model.

Abstract: Techniques for data classification using clustering. A method includes replacing a plurality of portions of metadata for a plurality of data objects with a plurality of replacement characters in order to generate a plurality of replaced strings; clustering the plurality of data objects into a plurality of clusters based on commonalities between the plurality of replaced strings of data objects of the plurality of data objects; classifying a subset of the data objects in each cluster into at least one class; and aggregating classes within at least one cluster of the plurality of clusters, wherein aggregating classes within each of the at least one cluster includes applying the at least one class for the subset of the data objects in each cluster to each other data object within the cluster.

Abstract: Techniques for data classification using clustering. A method includes replacing a plurality of portions of metadata for a plurality of data objects with a plurality of replacement characters in order to generate a plurality of replaced strings; clustering the plurality of data objects into a plurality of clusters based on commonalities between the plurality of replaced strings of data objects of the plurality of data objects; classifying a subset of the data objects in each cluster into at least one class; and aggregating classes within at least one cluster of the plurality of clusters, wherein aggregating classes within each of the at least one cluster includes applying the at least one class for the subset of the data objects in each cluster to each other data object within the cluster.