Abstract: Software on a website serves a user of an online content aggregation service a first article that the user views. The software extracts named entities from the first article using a named-entity recognizer. The named-entity recognizer uses a sequence of word embeddings as inputs to a conditional random field (CRF) tool to assign labels to each of the word embeddings. Each of the word embeddings is associated with a word in the first article and is trained using an entire topical article from a corpus of topical articles as a context for the word. The software then creates rankings for articles ingested by the content aggregation service based at least in part on the named entities and serves the user a second article using the rankings.

Abstract: Software for a website hosting a content-aggregation service generates a first representation of interests for a user. The first representation includes a plurality of entities including pivot entities and extended entities, where the extended entities result from a nearest-neighbor search of word embeddings. Each of the extended entities is associated with a nearness score that is weighted using a distance of the extended entity from one of the pivot entities. For each of a plurality of articles ingested by the content-aggregation service, the software generates a second representation that associates an aboutness score with each of the plurality of entities. The software uses the first representation, a similarity measure, and the second representations to create rankings of the plurality of articles. The software receives a request for access to the content-aggregation service from the user and serves the user a content stream based at least in part on the rankings.

Abstract: Software for a content-aggregation website generates a first representation of interests for a geographical location. The representation includes a plurality of entities that are derived from a corpus of documents. Each of the plurality of entities is associated with an expected value that is based on engagement signals from users in the geographical location and that is weighted using a sparse-polarity approach to be discriminative with respect to other entities. Each of the ingested articles is represented by the second representation that associates an aboutness score with each of the plurality of entities. The software uses the first representation, a similarity measure, and a second representation to create rankings of a plurality of ingested articles received. Then the software receives a request for access to the content-aggregation service from a new user from the geographical location and serves the new or infrequent user a content stream based on the rankings.

Abstract: A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for multi-label prediction comprises generating a label space; receiving a data point from a user; generating a first feature vector from the data point; projecting the first feature vector to the label space; determining a first set of labels associated with the first feature vector from the label space; converting the first set of labels to a second set of labels; and providing the second set of labels to the user.

Abstract: Methods and apparatus for detecting abusive language are disclosed. In one embodiment, a set of character N-grams is ascertained for a set of text. Feature values for a plurality of features of the set of text are determined, based, at least in part, on the set of character N-grams. A computer-generated model is applied to the feature values for the plurality of features to generate a score for the set of text, where the model includes a plurality of weights, each of the weights corresponding to one of the features. It may then be determined whether the set of text includes abusive language based, at least in part, on the score.

Abstract: Systems, methods, and apparatuses are disclosed for adaptively generating a summary of web-based content based on an attribute of a mobile communication device having transmitted a request for the web-based content. By adaptively generating the summary based on an attribute of the mobile communication device such as an amount of visual space available or a number of characters permitted in the interface, a display of the web-based content may be controlled on the mobile communication device in a way that was not previously available. This enables control of displaying web-based content that has been adaptively generated to be displayed on limited display screens based on a learned attribute of the mobile communication device requesting the web-based content.