Amazon Technologies, Inc.

Dianhuan Lin

Arpit Mittal

Petra Elisabeth Holmes

Mihai Valentin Tablan

David Spike Palfrey

Sina Samangooei

Emilio Fabio Monti

Benjamin David Scott

Pedro Ribeiro

A query parsing system uses a multi-stage process to parse the text of incoming queries before attempting to answer the queries. The multi-stage configuration involves a first trained classifier to determine the query type, or intent, of the text and a plurality of second trained classifiers, where each of the second trained classifiers is configured particularly for one specific respective query type. During query processing, the first trained classifier is used on the text to identify the query type. A second trained classifier for that specific identified query type is then found and used on the text to identify what strings in the text correspond to specific entities needed to resolve the query. The identified text strings and query type are then placed into a form understandable by a knowledge base and sent to the knowledge base for resolution. The classifiers may be trained using queries and answers previously processed by the knowledge base using a rules/template resolution process.

Multi-stage query processing

A voice-controlled question answering system that uses both a knowledge base and a detailed index of other sources that may be accessible over the Internet. The knowledge base is used to answer questions of more general interest whose answers are contained in the knowledge base. The index is used to answer more complex questions that are not answerable using the knowledge base. Web and other sources are analyzed to create the index, where text segments are indexed along with data describing the text segments in a quasi-semantic way. Quasi-semantic features are extracted from incoming spoken questions and used, along with machine learning trained models, to identify an indexed text segment that includes the answer to the question. The text segment may then be rearranged or simply passed to a speech synthesizer so the answer may be spoken aloud to a user.

Quasi-semantic question answering

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Cambridge, United Kingdom

Dianhuan Lin