Abstract: Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.

Abstract: Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.

Abstract: Representative embodiments disclose mechanisms to complete partial queries entered by a user. Users enter a partial query. The partial query is used to search a short text index comprising the titles of documents. The search yields a list results. The top k entries of the list are selected and a language model is created from the top k entries. The language model comprises n-grams from the top k entries and an associated probability for each n-gram. A query completion generator creates query completion suggestions by matching n-grams with the partial query, removing candidate suggestions that to not comply with suggestion rules, and filtering the remaining suggestions according to a filtering criteria. The top N results are returned as suggestions to complete the query.

Abstract: Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.

Abstract: Non-limiting examples of the present disclosure describe low latency pre-web classification of query data. In examples, processing is performed where query data may be analyzed in a low latency manner that includes providing a vertical intent classification and entity identification for query data before a web ranking service processes the query data. Query data may be received. A vertical intent classification index may be searched using the query data. In examples, the vertical intent classification index may comprise a set of files that can be used to determine one or more candidate entity identifiers for the query data. The one or more entity identifiers may be ranked. The query data, a vertical intent classification for the vertical intent classification index and the one or more ranked candidate entity identifiers may be transmitted for processing associated with a web ranking service. Other examples are also described.

Abstract: Representative embodiments disclose mechanisms to complete partial natural language questions. Users enter a partial question. The system comprises a plurality of indexes, one index comprising common phrases associated with natural language questions and other indexes comprising short text entries associated with documents, such as document titles. The partial question is used to search one or more of the indexes. The search yields a ranked list of results. The top k entries of the list are selected and one or more language models are created from the top k entries. Each language model comprises n-grams from the top k entries from an index and an associated probability for each n-gram. A question completion generator creates question completion suggestions by matching n-grams with the partial question, removing ungrammatical candidate suggestions, and filtering the remaining suggestions per a filtering criteria. The top N results are returned as suggestions to complete the question.

Abstract: Representative embodiments disclose mechanisms to complete partial queries entered by a user. Users enter a partial query. The partial query is used to search a short text index comprising the titles of documents. The search yields a list results. The top k entries of the list are selected and a language model is created from the top k entries. The language model comprises n-grams from the top k entries and an associated probability for each n-gram. A query completion generator creates query completion suggestions by matching n-grams with the partial query, removing candidate suggestions that to not comply with suggestion rules, and filtering the remaining suggestions according to a filtering criteria. The top N results are returned as suggestions to complete the query.

Abstract: Representative embodiments disclose mechanisms to complete partial natural language questions. Users enter a partial question. The system comprises a plurality of indexes, one index comprising common phrases associated with natural language questions and other indexes comprising short text entries associated with documents, such as document titles. The partial question is used to search one or more of the indexes. The search yields a ranked list of results. The top k entries of the list are selected and one or more language models are created from the top k entries. Each language model comprises n-grams from the top k entries from an index and an associated probability for each n-gram. A question completion generator creates question completion suggestions by matching n-grams with the partial question, removing ungrammatical candidate suggestions, and filtering the remaining suggestions per a filtering criteria. The top N results are returned as suggestions to complete the question.

Abstract: Non-limiting examples of the present disclosure describe low latency pre-web classification of query data. In examples, processing is performed where query data may be analyzed in a low latency manner that includes providing a vertical intent classification and entity identification for query data before a web ranking service processes the query data. Query data may be received. A vertical intent classification index may be searched using the query data. In examples, the vertical intent classification index may comprise a set of files that can be used to determine one or more candidate entity identifiers for the query data. The one or more entity identifiers may be ranked. The query data, a vertical intent classification for the vertical intent classification index and the one or more ranked candidate entity identifiers may be transmitted for processing associated with a web ranking service. Other examples are also described.