Kalina Bontcheva is senior researcher in the Natural Language Processing Group, Department of Computer Science, University of Sheffield (http://nlp.shef.ac.uk). Her research interests include personalised summarisation of social media, mining information from patents, sentiment analysis, and collaborative environments for text annotation.
At present, 200 million Twitter users send 140 million tweets a day, Facebook has 750 million active users, who spend over 700 billion minutes per month on the site, and increasingly knowledge is generated utilising the "wisdom of the crowd", on Wikipedia, Quora, and other similar sites. This unprecedented rise in the volume and importance of online textual content has resulted in companies and individuals increasingly struggling with information overload, or, as Clay Shirky defines it - a filter failure.
This talk will discuss how text analytics and natural language processing can help address these issues, through the development of methods capable of extracting useful knowledge from noisy, contradictory content; inferring an individual's information seeking goals; offering personalised information access, and making use of distributed human computation, by harnessing the knowledge of a large number of humans. I will also touch upon the challenge of developing research infrastructures for experimentation with large-scale Text-to-Knowledge (T2K) analytics, at affordable costs for research teams and companies.