Abstract: A system for disambiguation of noisy text includes an interface and processor. The interface is configured to receive a set of words. The processor is configured to determine a token from the set of words, wherein the token includes a subset of the set of words; determine candidates for the token; determine a ranking for the candidates based at least in part on a semantic similarity between the token and each of the candidates; determine whether to select a top ranked candidate or to abstain; and in response to a select determination to select the top ranked candidate, indicate the top ranked candidate is selected.

Abstract: Methods and systems for generating tailored user interface presentations based on microindustry clustering. According to various embodiments, the system accesses a set of entity profiles and a set of member profiles. The system determines a set of feature vectors for each entity of the set of entity profiles and identifies a set of movement data representing changes in association of one or more members from a first entity to a second entity. The system generates an entity graph for the set of entities and the set of members. The systems generate a first set of clusters in the entity graph, a second set of clusters by partitioning one or more of the first clusters, and a set of third clusters from the set of second clusters, combining one or more of the second clusters.

Abstract: Methods and systems for generating tailored user interface presentations based on microindustry clustering. According to various embodiments, the system accesses a set of entity profiles and a set of member profiles. The system determines a set of feature vectors for each entity of the set of entity profiles and identifies a set of movement data representing changes in association of one or more members from a first entity to a second entity. The system generates an entity graph for the set of entities and the set of members. The systems generate a first set of clusters in the entity graph, a second set of clusters by partitioning one or more of the first clusters, and a set of third clusters from the set of second clusters, combining one or more of the second clusters.

Abstract: For each of a group of nodes including a node corresponding to a first member and a plurality of nodes corresponding to members with which the first member is directly connected in a social graph, a plurality of reputation scores may be calculated for a node in the social graph, and the plurality of reputation scores for the node may be averaged into an importance score for the node. A keyness score for each of the plurality of nodes corresponding to members with which the first member is directly connected in the social graph can be calculated, the keyness score for each of the plurality of nodes being based on a connection strength for a connection between the node and the node corresponding to the first member and based on the importance score for the node. The keyness scores may be selected, averaged, and saved as a social capital score.

Abstract: For each of a group of nodes including a node corresponding to a first member and a plurality of nodes corresponding to members with which the first member is directly connected in a social graph, a plurality of reputation scores may be calculated for a node in the social graph, and the plurality of reputation scores for the node may be averaged into an importance score for the node. A keyness score for each of the plurality of nodes corresponding to members with which the first member is directly connected in the social graph can be calculated, the keyness score for each of the plurality of nodes being based on a connection strength for a connection between the node and the node corresponding to the first member and based on the importance score for the node. The keyness scores may be selected, averaged, and saved as a social capital score.