Attentive pseudo-relevance feedback network for query categorization

A system for providing results from a search engine, the system comprising:

• a non-transitory, computer-readable medium storing instructions;
• and
• a processor configured to execute the instructions to:
• generate an initial set based on a query received from a user;
• derive one or more fields corresponding to each document of the initial set;
• determine, by a model, a field embeddings representative of each of the one or more fields comprising:
• determining, by the model, a word-level embeddings and character-level embeddings for each word in the field,
• adjusting, by the model, the word-level embeddings based on the character-level embeddings, and
• determining, by the model, the field embeddings based on the adjusted word-level embeddings,
• wherein the adjusted word-level embeddings refine the field embeddings to compensate for morphological errors in the documents;
• generate, by the model, an attention value based on the query and on the field embeddings of the initial set;
• apply the attention value to the initial set to generate a set of results;
• determining a category based on an application of the attention value;
• and
• present the set of results in response to the query;
• wherein the set of results comprises one or more documents of the initial set determined based on the attention values and on the refined field embeddings.

A method of configuring a search engine, the method comprising:

• generating an initial set of results from a query;
• deriving a set of fields corresponding to each document of the initial set;
• determining, by a neural network, field embeddings representative of the set of fields corresponding to each document of the initial set comprising:
• determining, by the neural network, a word-level embeddings and character-level embeddings for each word in the set of fields,
• adjusting, by the neural network, the word-level embeddings based on the character-level embeddings, and
• determining, by the neural network, the field embeddings based on the adjusted word-level embeddings,
• wherein the adjusted word-level embeddings refine the field embeddings to compensate for morphological errors in the documents;
• extracting a subset of relevant documents from the initial set of results;
• generate a set attention value based on the query and on the field embeddings of the subset of relevant documents;
• generating a classification based on a combination of the set attention value and the subset of relevant documents;
• and
• presenting a revised set of results based on the classification;
• wherein the revised set of results comprises one or more documents of the initial set determined based on the set attention value and on the refined field embeddings.