Non-limiting examples of the present disclosure describe query classification to identify appropriateness of a query. A query may be received by at least one processing device. A deep neural network (DNN) model may be applied to evaluate the query. A vector representation may be generated for query based on application of the DNN model, where the DNN model is trained to classify queries according to a plurality of categories of appropriateness. The DNN model may be utilized to classify the query in a category of appropriateness based on analysis of the vector representation. In one example, auto-complete suggestions for the query may be filtered based on the classification of the category of appropriateness. In another example, classification of the query may be provided to an entry point. In yet another example, a response to the query is managed based on the classification of the query. Other examples are also described.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, at a processing device, a query; applying a deep neural network (DNN) model to evaluate the query, wherein the DNN is trained to classify queries according to a plurality of categories of appropriateness; generating a vector representation for query based on application of the DNN model; classifying, using the DNN model, the query in a category of appropriateness of the plurality of categories of appropriateness based on analysis of the vector representation; and filtering auto-complete suggestions for the query based on a determination of the category of appropriateness. 2. The method according to claim 1, wherein the DNN model is a deep structured semantic model (DSSM). 3. The method according to claim 2, wherein the classifying of the category of appropriateness for the query further comprises: computing distance values for the vector representation corresponding with each of the plurality of categories of appropriateness, and selecting the category of appropriateness from the plurality of categories of appropriateness based on a comparison of the distances values. 4. The method according to claim 1, wherein the filtering further comprises restricting output of an auto-complete suggestion based on the determination indicating that the query is classified as offensive. 5. A method comprising: receiving, at a processing device, a query; processing data of the query to generate one or more tri-gram representations from the data; applying a deep neural network (DNN) model to evaluate the one or more tri-gram representations, wherein the DNN is trained to classify queries according to a plurality of categories of appropriateness; generating, based on application of the DNN model, a vector representation for the one or more tri-gram representations; and classifying, using the DNN model, the query in a category of appropriateness of the plurality of categories of appropriateness based on analysis of the vector representation. 6. The method according to claim 5, further comprising determining a response to the query based on a classification of the category of appropriateness. 7. The method according to claim 6, wherein the response is at least one selected from a group consisting of: a contextual response based on the data of the query, an indication that the query is offensive, and ignoring the query in a case where the classification identifies the query as offensive. 8. The method according to claim 5, further comprising filtering auto-complete suggestions for the query based on a classification of the category of appropriateness. 9. The method according to claim 5, wherein the DNN model is a deep structured semantic model (DSSM). 10. The method according to claim 5, further comprising normalizing the data of the query to generate normalized data, wherein the normalized data is processed to generate the one or more tri-gram representations. 11. The method according to claim 5, wherein the determining of the category of appropriateness for the query further comprises: computing distance values for the vector representation corresponding with each of the plurality of categories of appropriateness, and selecting the category of appropriateness from the plurality of categories of appropriateness based on a comparison of the distances values. 12. The method according to claim 11, wherein the computing of the distance of the vector representation to the plurality of categories of appropriateness further comprises: computing Euclidean distance values for the vector representation relative to a centroid value associated with each of the plurality of categories of appropriateness, and wherein the selecting selects the category of appropriateness based on a comparison of the Euclidean distance values. 13. A system comprising: at least one processor; and a memory operatively connected with the at least one processor storing computer-executable instructions that, when executed by the at least one processor, causes the at least one processor to execute a method that comprises: receiving, at a processing device, a query, processing data of the query to generate one or more tri-gram representations from the data, applying a deep neural network (DNN) model to evaluate the one or more tri-gram representations, wherein the DNN is trained to classify queries according to a plurality of categories of appropriateness, generating, based on application of the DNN model, a vector representation for the one or more tri-gram representations, and classifying, using the DNN model, the query in a category of appropriateness of the plurality of categories of appropriateness based on analysis of the vector representation. 14. The system according to claim 13, wherein the method further comprises determining a response to the query based on a classification of the category of appropriateness. 15. The system according to claim 14, wherein the response is at least one selected from a group consisting of: a contextual response based on the data of the query, an indication that the query is offensive, and ignoring the query in a case where classification identifies the query as offensive. 16. The system according to claim 13, wherein the method further comprises filtering auto-complete suggestions for the query based on a classification of the category of appropriateness. 17. The system according to claim 13, wherein the DNN model is a deep structured semantic model (DSSM). 18. The system according to claim 13, wherein the method further comprises normalizing data of the query to generate normalized data, wherein the normalized data is processed to generate the one or more tri-gram representations. 19. The system according to claim 13, wherein the determining of the category of appropriateness for the query further comprises: computing distance values for the vector representation corresponding with each of the plurality of categories of appropriateness, and selecting the category of appropriateness from the plurality of categories of appropriateness based on a comparison of the distances values. 20. The system according to claim 19, wherein the computing of the distance of the vector representation to the plurality of categories of appropriateness further comprises: computing Euclidean distance values for the vector representation relative to a centroid value associated with each of the plurality of categories of appropriateness, and wherein the selecting selects the category of appropriateness based on a comparison of the Euclidean distance values.