Microsoft Technology Licensing, LLC

Ryan Frederick Stewart

Yi-Min Wang

Girish Kumar

Xiaoxin Yin

Fang Liu

Junhua Wang

Vijay Nair

Tiffany Kumi Dohzen

Systems and methodologies for improved query classification and processing are provided herein. As described herein, a query prediction model can be constructed from a set of training data (e.g., diagnostic data obtained from an automatic diagnostic system and/or other suitable data) using a machine learning-based technique. Subsequently upon receiving a query, a set of features corresponding to the query, such as the length and/or frequency of the query, unigram probabilities of respective words and/or groups of words in the query, presence of pre-designated words or phrases in the query, or the like, can be generated. The generated features can then be analyzed in combination with the query prediction model to classify the query by predicting whether the query is aimed at a head Uniform Resource Locator (URL) or a tail URL. Based on this prediction, an appropriate index or combination of indexes can be assigned to answer the query.

System for finding queries aiming at tail URLs

Computer-readable media, systems, and methods for augmenting URL queries are described. In embodiments, a URL query is received from a user and it is determined whether the URL query is a simple URL query. Further, if the URL query is a simple URL query, an augmented query is created by word-breaking at least a portion of the URL query and the augmented query is associated with one or more ranking preferences. In various other embodiments, a URL query is received from a user and it is determined whether the URL query is a complex URL query. Further, if the URL query is a complex URL query, an augmented query is created that is identical to the URL query and the augmented query is associated with one or more ranking preferences.

Augmenting URL queries

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Snoqualmie, WA, United States of America

Ryan Frederick Stewart