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US 12,657,227 B2Granted
Machine learning based spend classification
AssigneeOracle International Corporation
InventorsAkash Baviskar, Krishnan Ramanathan, Abhilash Neog, Dipawesh Pawar, Karthik Bangalore Mani
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Abstract
Embodiments classify a product to a product category. Embodiments receive a textual description of the product and create an index of product categories. Embodiments use a plurality of classifiers to classify the textual description to one of the product categories, the classifiers including an index based classifier, an encyclopedia based classifier, a Bayes' rule based classifier, and an embeddings classifier.
Claims20
A method of classifying a product to a product category, the method comprising:
- receiving a textual description of the product;
- creating an index of product categories;
- sequentially inputting the entire textual description to a sequence of different types of classifiers until the textual description is classified, with a confidence score above a predefined threshold, as one of the product categories, wherein the confidence score is determined for each classification output by one of the sequence of classifiers, and a determination is made after every output of the classifier, except a last classifier of the sequence, whether to input the textual description to the next classifier of the sequence of classifiers based on the confidence score;
- wherein a first plurality of the classifiers in a beginning of the sequence are unsupervised classifiers, followed by one or more supervised classifiers;
- wherein a first textual description corresponding to a first product is classified after being input to each of the first plurality of the classifiers without being input to the one or more supervised classifiers;
- wherein a second textual description corresponding to a second product is classified after being input to the first plurality of the classifiers and the one or more supervised classifiers;
- wherein the sequence of classifiers comprise a plurality of syntactic match classifiers and a plurality of semantic match classifiers;
- wherein the unsupervised classifiers comprise an index based classifier followed by an encyclopedia based classifier and the one or more supervised classifiers comprise a Bayes' rule based classifier followed by an embeddings classifier.
5 more elements
A non-transitory computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to classify a product to a product category, the classifying comprising:
- receiving a textual description of the product;
- creating an index of product categories;
- sequentially inputting the entire textual description to a sequence of different types of classifiers until the textual description is classified, with a confidence score above a predefined threshold, as one of the product categories, wherein the confidence score is determined for each classification output by one of the sequence of classifiers, and a determination is made after every output of the classifier, except a last classifier of the sequence, whether to input the textual description to the next classifier of the sequence of classifiers based on the confidence score;
- wherein a first plurality of the classifiers in a beginning of the sequence are unsupervised classifiers, followed by one or more supervised classifiers;
- wherein a first textual description corresponding to a first product is classified after being input to each of the first plurality of the classifiers without being input to the one or more supervised classifiers;
- wherein a second textual description corresponding to a second product is classified after being input to the first plurality of the classifiers and the one or more supervised classifiers;
- wherein the sequence of classifiers comprise a plurality of syntactic match classifiers and a plurality of semantic match classifiers;
- wherein the unsupervised classifiers comprise an index based classifier followed by an encyclopedia based classifier and the one or more supervised classifiers comprise a Bayes' rule based classifier followed by an embeddings classifier.
5 more elements
A spend classification system comprising:
- one or more processors configured to classify a product to a product category by:
- receiving a textual description of the product;
- creating an index of product categories;
- sequentially inputting the entire textual description to a sequence of different types of classifiers until the textual description is classified, with a confidence score above a predefined threshold, as one of the product categories, wherein the confidence score is determined for each classification output by one of the sequence of classifiers, and a determination is made after every output of the classifier, except a last classifier of the sequence, whether to input the textual description to the next classifier of the sequence of classifiers based on the confidence score;
- wherein a first plurality of the classifiers in a beginning of the sequence are unsupervised classifiers, followed by one or more supervised classifiers;
- wherein a first textual description corresponding to a first product is classified after being input to each of the first plurality of the classifiers without being input to the one or more supervised classifiers;
- wherein a second textual description corresponding to a second product is classified after being input to the first plurality of the classifiers and the one or more supervised classifiers;
- wherein the sequence of classifiers comprise a plurality of syntactic match classifiers and a plurality of semantic match classifiers;
- wherein the unsupervised classifiers comprise an index based classifier followed by an encyclopedia based classifier and the one or more supervised classifiers comprise a Bayes' rule based classifier followed by an embeddings classifier.
6 more elements
Dependencies are inferred from the claim text.