Amazon Technologies, Inc.

Tony Chun Tung Ng

Rashmi Gangadharaiah

Deepak Seetharam Nadig

Ankit Kapoor

Sharon Shapira

Kasturi Bhattacharjee

Senthil Chock Chidambaram

Senthil C Chidambaram

Systems and methods are used to detect underlying themes from a collection of documents at an aggregated level. A representative set of documents may be selected from a cluster of documents, with the representative set of documents corresponding to a general theme of the cluster. Candidate theme phrases may then be extracted from the documents and used to generate document embeddings and candidate phrase embeddings, which may be ranked, such as with a diversity-based ranking approach. Certain candidates may be selected from the ranking. Each of the documents forming the representative set may then be concatenated and a query embedding may be generated and ranked against the candidate phrases. In this manner, a collection of phrases associated with both the general underlying theme of the cluster, along with granular topics associated with that theme, may be identified.

Theme detection within a corpus of information

The present disclosure generally relates to a feedback processing service that can receive customer input, as customer feedback corresponds to a service context. The feedback processing service aggregates semantically similar feedback as a cluster. Then, the feedback processing service can prioritize each of the clusters by ranking each of the clusters.

Feedback prioritization system

Growing community of inventors

San Ramon, CA, United States of America

Tony Chun Tung Ng