Microsoft Technology Licensing, LLC

Gonzalo Jorge Aniano Porcile

Huiji Gao

Yu Gan

Liang Zhang

Jun Shi

Bo Long

Haichao Wei

Abhimanyu Lad

Yu Liu

Qin Iris Wang

Michaeel M Kazi

Yuwei Qiu

Xiaowei Liu

Ying Xiong

Embodiments of the described technologies are capable of reading a text sequence that include at least one word; extracting model input data from the text sequence, where the model input data includes, for each word of the text sequence, segment data and non-segment data; using a first machine learning model and at least one second machine learning model, generating, for each word of the text sequence, a multi-level feature set; outputting, by a third machine learning model, in response to input to the third machine learning model of the multi-level feature set, a tagged version of the text sequence; executing a search based at least in part on the tagged version of the text sequence.

Neural tagger with deep multi-level model

The disclosed embodiments provide a system for performing a natural language search. During operation, the system applies a first machine learning model to a natural language query to predict one or more search intentions associated with the natural language query. Next, the system applies a second machine learning model to the natural language query to produce one or more search parameters associated with a first intention in the search intention(s), wherein the search parameter(s) include a field and a value of the field. The system then performs a first search of a first vertical associated with the first intention using the search parameter(s). Finally, the system generates a ranking containing a first set of search results from the first search of the first vertical and outputs at least a portion of the ranking in a response to the natural language query.

Vertical processing of natural language searches

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Sunnyvale, CA, United States of America

Gonzalo Jorge Aniano Porcile