Amazon Technologies, Inc.

Gregory David Adams

Bing Xiang

Stephen Michael Ash

Zhiguo Wang

Patrick Ng

Sudipta Sengupta

Ramesh M Nallapati

Mukul Vijay Karnik

Timothy Jones

Douglas Scott Cerny

Sharanabasappa Parashuram Revadigar

Nanyan Li

Hanbo Li

Yung Haw Wang

Stephanie Weeks

Deepak Shantha Murthy

Anuj Chauhan

Henghui Zhu

Rishav Chakravarti

Jiarong Jiang

Wuwei Lan

Sheng Zhang

Techniques for handling natural language query processing are described. In some examples, entities are recognized during an entity recognition phase and then relations between those entities are determined. Those relations are fed to an entity linker to help the linker link candidate to columns and/or a intent representation generator to help parse multiple values and column pairs of a natural language query.

Natural language query processing

Techniques for handling natural language query processing are described. In some examples, a sequence-to-sequence model is used to handle a natural language query. Post-processing of a result of the sequence-to-sequence model utilizes fine-grained information from an entity linker. In some examples, the sequence-to-sequence model and aspects of a natural language query pipeline are used to handle a natural language query.

Interactive assistances for executing natural language queries to data sets may be performed. A natural language query may be received. Candidate entity linkages may be determined between an entity recognized in the natural language query and columns in data sets. The candidate linkages may be ranked according to confidence scores which may be evaluated to detect ambiguity for an entity linkage. Candidate entity linkages may be provided to a user via an interface to select an entity linkage to use as part of completing the natural language query.

Interactive assistance for executing natural language queries to data sets

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Seattle, WA, United States of America

Gregory David Adams