Classification of search queries
First Claim
1. A computer-implemented method of classifying a search query in a network, the method comprising:
- obtaining, from the network, at least one search result for each of a plurality of search queries, wherein the plurality of search queries includes at least one unseen search query;
modifying at least one of the plurality of search queries comprising at least one unseen search query;
with a processing module, executing on one or more computing devices, automatically;
classifying the plurality of search queries into one or more categories, the classifying comprising;
applying one or more predetermined rules to each of the plurality of search queries, wherein the one or more predetermined rules are indicative of the one or more categories and each of the plurality of search queries is associated with one or more search results in the network;
determining, for each of the plurality of search queries, one or more similarity values indicating similarity to each of the one or more categories based on the applied one or more predetermined rules;
with a training module, executing on one or more computing devices, automatically;
training a machine learning module using the classified search queries including the at least one unseen search query, the training comprising;
modifying at least one of the plurality of classified search queries in order to change a plurality of training sets to be applied to the training module;
obtaining a training set from the plurality of training sets;
obtaining each predetermined rule from the one or more predetermined rules;
applying the each predetermined rule to the training set to determine a similarity of the training set to the one or more categories;
evaluating the similarities for the training set to the one or more categories to classify the training set, wherein each training set is classified to the one or more categories;
converting each of the plurality of training sets and associated similarity values into a number or a vector;
determining whether the each of the plurality of training sets is within a range suitable for the machine learning module;
applying the machine learning module to the plurality of training sets, wherein the each of the plurality of training sets is based on one of the plurality of classified search queries and at least one of the respective one or more similarity valuesreceiving the at least one unseen search query; and
classifying the at least one unseen search query into a category of the one or more categories by applying the trained machine learning module.
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Abstract
A computer-implemented method of classifying a search query in a network comprises: classifying a plurality of search queries into categories, comprising: applying predetermined rules to each of the plurality of search queries, wherein the predetermined rules are indicative of the categories and each of the plurality of search queries is associated with search results in the network; determining, for each of the plurality of search queries, similarity values indicating similarity to each of the categories based on the applied predetermined rules; and training a machine learning module, comprising: applying the machine learning module to a plurality of training sets to a plurality of training sets, wherein each of the plurality of training sets is based on one of the plurality of classified search queries and at least one of the respective one or more similarity values, a corresponding system, computing device and non-transitory computer-readable storage medium.
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Citations
19 Claims
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1. A computer-implemented method of classifying a search query in a network, the method comprising:
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obtaining, from the network, at least one search result for each of a plurality of search queries, wherein the plurality of search queries includes at least one unseen search query; modifying at least one of the plurality of search queries comprising at least one unseen search query; with a processing module, executing on one or more computing devices, automatically; classifying the plurality of search queries into one or more categories, the classifying comprising; applying one or more predetermined rules to each of the plurality of search queries, wherein the one or more predetermined rules are indicative of the one or more categories and each of the plurality of search queries is associated with one or more search results in the network; determining, for each of the plurality of search queries, one or more similarity values indicating similarity to each of the one or more categories based on the applied one or more predetermined rules; with a training module, executing on one or more computing devices, automatically; training a machine learning module using the classified search queries including the at least one unseen search query, the training comprising; modifying at least one of the plurality of classified search queries in order to change a plurality of training sets to be applied to the training module; obtaining a training set from the plurality of training sets; obtaining each predetermined rule from the one or more predetermined rules; applying the each predetermined rule to the training set to determine a similarity of the training set to the one or more categories; evaluating the similarities for the training set to the one or more categories to classify the training set, wherein each training set is classified to the one or more categories; converting each of the plurality of training sets and associated similarity values into a number or a vector; determining whether the each of the plurality of training sets is within a range suitable for the machine learning module; applying the machine learning module to the plurality of training sets, wherein the each of the plurality of training sets is based on one of the plurality of classified search queries and at least one of the respective one or more similarity values receiving the at least one unseen search query; and classifying the at least one unseen search query into a category of the one or more categories by applying the trained machine learning module. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 19)
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16. A computing device for classifying a search query, the computing device comprising:
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one or more processors, configured to perform operations; and a memory, coupled to the one or more processors and comprising instructions to cause, when executing on the one or more processors, the computing device to perform operations, comprising; obtaining, from the network, at least one search result for each of a plurality of search queries, wherein the plurality of search queries includes at least one unseen search query; modifying at least one of a plurality of search queries comprising at least one unseen search query; classifying the plurality of search queries into one or more categories, the classifying comprising; applying one or more predetermined rules to each of the plurality of search queries, wherein the one or more predetermined rules are indicative of the one or more categories and each of the plurality of search queries is associated with one or more search results in the network; determining, for each of the plurality of search queries, one or more similarity values indicating similarity to each of the one or more categories based on the applied one or more predetermined rules; training a machine learning module using the classified search queries including the at least one unseen search query, the training comprising; modifying at least one of the plurality of classified search queries in order to change a plurality of training sets to be applied to the training module; obtaining a training set from the plurality of training sets; obtaining each predetermined rule from the one or more predetermined rules; applying the each predetermined rule to the training set to determine a similarity of the training set to the one or more categories; evaluating the similarities for the training set to the one or more categories to classify the training set, wherein each training set is classified to the one or more categories; converting each of the plurality of training sets and associated similarity values into a number or vector; determining whether the each of the plurality of training sets is within a range suitable for the machine learning module; applying the machine learning module to a plurality of training sets, wherein each of the plurality of training sets is based on one of the plurality of classified search queries and at least one of the respective one or more similarity values; receiving the at least one unseen search query; and classifying the at least one unseen search query into a category of the one or more categories by applying the trained machine learning module. - View Dependent Claims (17, 18)
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Specification