Amazon Technologies, Inc.

Jun Wang

Sudipta Sengupta

Bing Xiang

Ramesh M Nallapati

Zhiguo Wang

Sharanabasappa Parashuram Revadigar

Stephen Michael Ash

Timothy Jones

Patrick Ng

Hanbo Li

Rishav Chakravarti

Jiarong Jiang

Donald Harold Rivers Weidner

Yung Haw Wang

A schema and cell value aware Named Entity Recognition (NER) model is used to perform natural language queries. Natural language queries may be received via an interface of a natural language query processing system. A fuzzy search may be performed that allows non-exact matches for column names or cell values of data sets potentially used to answer the natural language query. An NER model that adds a type embedding for an exact match of a column name or cell found in the fuzzy search that corresponds to a span of one or more words may be applied as part of generating the entity prediction for the natural language query. One or more queries to at least one of the data sets may be performed to return a result to the natural language query using the entity prediction generated by the NER machine learning model.

Schema and cell value aware named entity recognition model for executing natural language queries

Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Multiple stage filtering for natural language query processing pipelines

Query restatements may be provided for explaining natural language query results. A natural language query is received at a natural language query processing system. An intermediate representation of the natural language query is generated for executing the natural language query. The intermediate representation is translated into a natural language restatement of the natural language query. The natural language restatement is provided with a result of the natural language query via an interface of the natural language query processing system.

Providing query restatements for explaining natural language query results

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Jersey City, NJ, United States of America

Jun Wang