Abstract: Described herein is a technique for mapping the raw text of a job title of an online job posting to an entity embedding, associated with an entity or entry of a title taxonomy. The raw text of the job title is first encoded to generate a multilingual word embedding in a multilingual word embedding space. Then, the vector representation of the job title, as represented in the multilingual word embedding space is translated, using a neural network, to a vector representation of the job title in the entity embedding space. Finally, a nearest neighbor search is performed to identify an entity embedding associated with an entity or entry in the title taxonomy that has a vector representation that is closest in distance to the vector output by the neural network.

Abstract: Disclosed is a novel system and method to select software components. A set of available software components are accessed. Next, one or more dimensions are defined. Each dimension is an attribute to the set of available software components. A set of coherence distances between each pair of the available software components in the set of available software components is calculated for each of the dimensions that have been defined. Each of the coherence distances are combined between each pair of the available software components that has been calculated in the set of the coherence distances into an overall coherence degree for each of the available software components. Using the overall coherence degree, one or more software components are selected to be included in a software bundle.