Abstract: In one embodiment, a method includes receiving a request to identify k steps for a particular entity to acquire a target attribute in public sentiments, accessing a table of word vector relationships, looking up an entity word vector corresponding to the entity name and a target attribute word vector corresponding to the n-gram representing the target attribute using the table, determining a directional vector in the d-dimensional embedding space that connects from the entity word vector to the target attribute word vector, identifying k points on the directional vector that evenly split the directional vector into k+1 segments, selecting, for each of the k points, a word vector that is closest to the point, identifying, for each of the k selected word vectors, a corresponding n-gram by looking up the word vector in the table, and sending a response message comprising the k identified n-grams.

Abstract: In one embodiment, a method includes receiving a request to identify k steps for a particular entity to acquire a target attribute in public sentiments, accessing a table of word vector relationships, looking up an entity word vector corresponding to the entity name and a target attribute word vector corresponding to the n-gram representing the target attribute using the table, determining a directional vector in the d-dimensional embedding space that connects from the entity word vector to the target attribute word vector, identifying k points on the directional vector that evenly split the directional vector into k+1 segments, selecting, for each of the k points, a word vector that is closest to the point, identifying, for each of the k selected word vectors, a corresponding n-gram by looking up the word vector in the table, and sending a response message comprising the k identified n-grams.

Abstract: In one embodiment, a method includes receiving a request to identify k steps for a particular entity to acquire a target attribute in public sentiments, accessing a table of word vector relationships, looking up an entity word vector corresponding to the entity name and a target attribute word vector corresponding to the n-gram representing the target attribute using the table, determining a directional vector in the d-dimensional embedding space that connects from the entity word vector to the target attribute word vector, identifying k points on the directional vector that evenly split the directional vector into k+1 segments, selecting, for each of the k points, a word vector that is closest to the point, identifying, for each of the k selected word vectors, a corresponding n-gram by looking up the word vector in the table, and sending a response message comprising the k identified n-grams.