#41: MLDublin meets @ Linkedin

Professional opportunities can manifest itself in several ways like finding a new job, enhancing or learning a new skill through an online course, connecting with someone who can help with new professional opportunities in the future, finding insights about a lead to close a deal, sourcing the best candidate for a job opening, consuming the best professional news to stay informed, and many others. LinkedIn is the largest online professional social network that connects talent with opportunity at scale by leveraging and developing novel AI methods. In this talk, I will provide an overview of how AI is used across LinkedIn and the challenges thereof. The talk would mostly emphasize the principles required to bridge the gap between theory and practice of AI, with copious illustrations from the real world.

Introducing a distinction between thematic and taxonomic word embeddings. Based on this distinction it will then contrast and explain the performance of a range of embeddings across a number of semantic relatedness evaluation datasets. A standard approach to creating taxonomic embeddings is to use a random walk across a taxonomy to generate a corpus and then to train the embeddings using this corpus. Building on this approach, the talk will present a series of experiments that first explore how the properties of a taxonomy affect the scaling properties of corpora generated from it, and hence the properties of the resulting embeddings, but also how the results of blending natural and synthesised corpora are affected by the relative sizes of the corpora. The talk presents works done jointly with Magdalena Kacmajor, Alfredo Maldonado and Filip Klubička.

Businesses are increasingly reliant on 3rd party data and information for making optimal decisions. News is a highly rich source of information about events taking place all around the world in real time, yet it has remained widely untapped in business applications due to (i) the large volume of news and media content; and (ii) the complexity of human languages and the challenges it creates for automation and scaling. The big data wave of the early 2010's and the deep learning wave of 2013 onwards have provided us with the tools we need to tackle these two issues. In this talk I will explain how our News Intelligence platform combines NLP and big data technology to extract high quality signals from news.