The growth and worldwide success of Twitter has surfaced natural limitations of hashtags and keyword searches. What was once a mechanism for organizing information for efficient consumption has been made impractical by overwhelming volume and diversity of discussion. Word embedding technology promises to change real-time analytics as much as it has effected static data analytics by making it possibly to follow events as they change in real time. But what does that mean, and what does big text look like? We’ll explore how using this new type of machine learning on streaming data allows for an evolving look at unfolding events with case studies of the ISIS “topic of the moment” and fear around the Zika virus outbreak. Beyond that, we’ll touch on the challenges of running text analytics for the 2014 World Cup (the biggest social media event yet) and discuss lessons learned and techniques used in order to cover the games in real time.
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Keynote: Cracking the Code to the Next Breakthrough in Machine Translation
Everyone is looking for the next breakthrough in machine translation. No one believes that machine translation is a completely solved problem. Most people would like to see machine translation systems produce higher quality results.
A good translation is one where the meaning of the source is preserved, and it is rendered correctly in the target language. Users expect accuracy on all of the various levels–grammar, syntax, semantics and pragmatics.
Dr. O’Neill-Brown has been carrying out research and development aimed at improving machine translation accuracy. Her work includes the development of novel methods that take advantage of semantics and pragmatics to increase user understanding of machine translated results. This talk will present the results of these research and development efforts. Dr. O’Neill-Brown will discuss how these techniques aid user understanding of machine translated results and boost machine translation quality.