Tableau Software, Inc.

Qixiang Zhang

Alex Djalali

Ryan Andrew Atallah

Yukiko Ishida Anonuevo

Amar Singh Lakhian

Andre Lukas Schorlemmer

A computer device receives a user input to specify a natural language command. In response to receiving the user input, the computer device generates a semantic interpretation for the natural language command using a trained word similarity model, based on semantic annotations for a published data source. The computer device queries the published data source based on the semantic interpretation, thereby retrieving a dataset. The computer device generates and displays a data visualization based on the retrieved dataset.

Automatic synonyms using word embedding and word similarity models

The various implementations described herein include methods and devices for recommending phrases for data visualizations. In one aspect, a method includes presenting a data visualization page to a user, the page including a first region for displaying a data visualization and a second region for phrase recommendations. The method further includes obtaining a dataset selected by the user, the dataset including a plurality of fields; and generating a first set of phrase recommendations based on the dataset, each phrase recommendation corresponding to a respective field. The method also includes displaying the first set of phrase recommendations in the second region; and receiving a user selection of a first phrase. The method further includes, in response to the user selection: presenting a data visualization in the first region using the first phrase; and displaying a second set of phrase recommendations generated based on the first phrase.

Phrase recommendations for data visualizations

The various implementations described herein include methods and devices for constructing phrases for data visualizations. In one aspect, a method includes presenting a data visualization page to a user including a visualization region and a phrase region; obtaining a dataset including a plurality of fields; displaying a phrase affordance for constructing a phrase; in response to a user selection of the phrase affordance, presenting a phrase construction menu including a set of fields; in response to a user selection of a field, presenting a set of aggregation operators selected based on a field type of the field; in response to a user selection of an aggregation operator, generating a first phrase using the selected field and the selected aggregation operator; presenting a data visualization generated using the first phrase; and displaying the first phrase as an active phrase in the phrase region.

Phrase builder for data analytic natural language interface

A method processes natural language commands (e.g., for data visualization). The method includes obtaining word embeddings (e.g., word vectors) for a set of words (or phrases) of a natural language. In some implementations, the set of words includes high-frequency or common words of the natural language. The method also includes training a word similarity model to identify similar words based on the word embeddings and a synonym database (a large lexical database, such as a thesaurus). The method also includes generating semantic annotations for a published data source using the trained word similarity model, based on the synonym database, and the word embeddings. The method also includes generating a semantic interpretation for a natural language command based on the semantic annotations for the published data source. The method also includes querying the published data source based on the sematic interpretation, thereby retrieving a dataset.

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Sunnyvale, CA, United States of America

Qixiang Zhang