Systems and methods are provided for suggesting actions for selected text based on content displayed on a mobile device. An example method can include converting a selection made via a display device into a query, providing the query to an action suggestion model that is trained to predict an action given a query, each action being associated with a mobile application, receiving one or more predicted actions, and initiating display of the one or more predicted actions on the display device. Another example method can include identifying, from search records, queries where a website is highly ranked, the website being one of a plurality of websites in a mapping of websites to mobile applications. The method can also include generating positive training examples for an action suggestion model from the identified queries, and training the action suggestion model using the positive training examples.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A mobile device comprising: a display device; at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the mobile device to: convert a selection made via the display device into a query, provide the query to an action suggestion model, the action suggestion model being trained to predict an action given a query, each action being associated with a mobile application, receive one or more predicted actions, and initiate display of the one or more predicted actions on the display device. 2. The mobile device of claim 1, wherein the memory further stores instructions that, when executed by the at least one processor, cause the mobile device to: receive a selection of one of the one or more predicted actions; and initiate an intent using the query for the mobile application associated with the selection. 3. The mobile device of claim 1, wherein initiating display of the one or more predicted actions includes for each predicted action, displaying an icon for the mobile application associated with the predicted action. 4. The mobile device of claim 3, wherein the icons for the one or more predicted actions overlay content displayed on the display device. 5. The mobile device of claim 1, wherein the selection is an image and converting the selection into a query includes: identifying text related to the image; and using at least some of the text as the query. 6. The mobile device of claim 1, wherein the selection is an image and converting the selection into a query includes: identifying an entity in the image; and using the entity as the query. 7. The mobile device of claim 1, wherein the memory further stores instructions that, when executed by the at least one processor, causes the mobile device to: receive the action suggestion model from a server; identify whitelisted websites in search records for a user of the mobile device; generate training examples from queries that include the whitelisted websites as highly ranked, the training examples having a weight higher than a ranking of the whitelisted website in the search records; and train the action suggestion model using the training examples, resulting in a personalized action suggestion model. 8. The mobile device of claim 1, wherein the selection is a voice command and converting the selection into a query includes: performing word recognition on the voice command; and using recognized words as the query. 9. The mobile device of claim 1, wherein converting the selection into a query includes: determining extensions for the selection to generate at least one extended query; providing the extended query to the an action suggestion model to generate second predicted actions; initiating display of the at least one extended query; and responsive to receiving a selection of the extended query, initiating display of second predicted actions. 10. The mobile device of claim 1, wherein at least one of the mobile applications is not installed on the mobile device. 11. A method comprising: identifying, from search records, queries where a website is highly ranked in corresponding search results, the website being one of a plurality of websites in a mapping of websites to mobile applications; generating positive training examples for an action suggestion model from the identified queries, each positive training example having the query, the website, a weight corresponding to a rank of the website in search results for the query, and a mobile application; and training the action suggestion model using the positive training examples. 12. The method of claim 11, further comprising: identifying a website in a manifest of a first mobile application; and adding the website and the first mobile application to the mapping. 13. The method of claim 11, further comprising: clustering similar mobile applications in a web store, the first mobile application appearing in a first cluster; identifying a query where the website is highly ranked; verifying that a search result for the query generated by the first mobile application is similar to a search result for the query generated by a second mobile application, the second mobile application being in the first cluster; and adding, responsive to the verifying, the website and the second mobile application to the mapping. 14. The method of claim 11, further comprising: personalizing the model based on search records stored on a mobile device. 15. The method of claim 11, further comprising: pushing the model to a client device; and personalizing the model based on search records associated with a user of the client device. 16. A method comprising: converting, responsive to receiving an action suggestion indication, a selection made via a display device of a mobile computing device into a query; predicting at least one mobile application for the query using an action suggestion model, the action suggestion model being trained to predict mobile applications given a query; and initiating display of a selectable control for the at least one mobile application on the display device. 17. The method of claim 16, further comprising: receiving a selection of the selectable control; and initiating a search intent using the query to the mobile application. 18. The method of claim 16, wherein initiating display of the selectable control includes displaying an icon for the mobile application. 19. The method of claim 16, wherein converting the selection into a query includes: determining extensions for the selection to generate at least one extended query; providing the extended query to the an action suggestion model to generate a second mobile application; initiating display of the at least one extended query; and responsive to receiving a selection of the extended query, initiating display of a selectable control for the second mobile application. 20. The method of claim 16, wherein at least one of the mobile application is not installed on the mobile device and the method further includes: receiving a selection of the selectable control; and initiating installation of the mobile application on the mobile device.