International Business Machines Corporation

Microsoft Technology Licensing, LLC

Daria Bogdanova

Matthias Kormaksson

Bianca Zadrozny

Cicero Nogueira Dos Santos

Juan Pablo Bottaro

Luciano De Andrade Barbosa

Maria Laura Jedrzejowska

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.

Risk-aware entity linking

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).

Machine learning and training a computer-implemented neural network to retrieve semantically equivalent questions using hybrid in-memory representations

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Dublin, Ireland

Daria Bogdanova