Abstract: In an embodiment, the disclosed technologies include identifying a content item of a first digital data source as a candidate for linking with a target entity of a second digital data source by matching a candidate entity mentioned in the content item to the target entity in accordance with semantic similarity data computed between the candidate entity and the target entity; inputting at least one feature of the content item and at least one feature of the target entity to a set of digital models that analyze the at least one feature of the content item and the at least one feature of the target entity and determine and output qualitative data; based on the qualitative data, determining link risk data; based on the link risk data and the semantic similarity data, and determining whether to generate a link between the content item and the target entity.

Abstract: In an embodiment, the disclosed technologies include identifying a content item of a first digital data source as a candidate for linking with a target entity of a second digital data source by matching a candidate entity mentioned in the content item to the target entity in accordance with semantic similarity data computed between the candidate entity and the target entity; inputting at least one feature of the content item and at least one feature of the target entity to a set of digital models that analyze the at least one feature of the content item and the at least one feature of the target entity and determine and output qualitative data; based on the qualitative data, determining link risk data; based on the link risk data and the semantic similarity data, and determining whether to generate a link between the content item and the target entity.

Abstract: Method and system to use supervised explicit semantic analysis in modeling semantics of a document, referred to as Supervised Explicit Semantic Analysis (SESA or Supervised ESA) is described. The SESA model includes an encoder that maps an object to a latent space, a knowledge base that provides the explicit categories, a projector that projects the latent representations to the explicit space, and similarity scorer that estimates the similarity between objects in the explicit space. In the context of an on-line social network system, the SESA model can be used beneficially to determine similarity between member profiles and electronic job postings with respect to skill entities stored in the on-line social network system.

Abstract: Determining semantically equivalent text or questions using hybrid representations based on neural network learning. Weighted bag-of-words and convolutional neural networks (CNN) based distributed vector representations of questions or text may be generated to compute the semantic similarity between questions or text. Weighted bag-of-words and CNN based distributed vector representations may be jointly used to compute the semantic similarity. A pair-wise ranking loss function trains neural network. In one embodiment, the parameters of the system are trained by minimizing a pair-wise ranking loss function over a training set using stochastic gradient descent (SGD).

Abstract: Methods and systems are provided for expanding an electronic word list, containing a set of words where each word is associated with a label from a first set of labels. A subset of training data containing a set of texts having a second set of labels is obtained. For each word in the electronic word list and a label in the sub-set of the training data, a feature selection criterion is calculated. One or more words are selected, for which resulting value of the feature selection criterion calculation is greater than a predetermined threshold value. The one or more selected words are added to the electronic word list.