Talk at Café Scientifique, Manchester
2016-07-20
Prof. Sophia Ananiadou, Director of NaCTeM, has been invited by Café Scientifique Manchester to give a talk on text mining, entitled Text Mining: Finding the needle in the haystack you didn't even know was there!. The talk, followed by a discussion, is free of charge and open to all. It will take place from 19:30 - 21:30 on Thursday 28th July at MadLab, 36-40 Edge Street, Northern Quarter, M4 1HN.
Café Scientifique is a global grassroots organisation, running across the world for more than ten years, which aims to improve communication between scientists and the general public and generally stimulate an interesting discussion.
Abstract
With the massive amount of knowledge recorded in unstructured textual form, we need automated methods that can extract and manage it. Text mining is a relatively new area of research used to extract automatically nuggets of information hidden in text and to present the distilled knowledge to users in a concise and meaningful manner. The National Centre for Text Mining (located at the School of Computer Science, The University of Manchester) has developed a number of text mining services to support a wide variety of users who are struggling to locate useful information from big textual data for their needs. Join this session with Professor Sophia Ananiadou and find out how computers can help us digest large amount of information!
Previous item | Next item |
Back to news summary page |
Featured News
- Talk at Generative AI Summit
- Talk at Open Data Science Conference (ODSC)
- BioLaySumm 2023 - Shared Task @ BioNLP 2023
- Prof. Ananiadou appointed as Senior Area Chair for ACL 2023
- Recent funding successes for Prof. Sophia Ananiadou
- Junichi Tsujii awarded Order of the Sacred Treasure, Gold Rays with Neck Ribbon
Other News & Events
- Prof. Ananiadou gives talk as part of Women in AI speaker series
- New Knowledge Knowledge Transfer Partnership with 10BE5
- Keynote Talk at the Festival of AI
- New article on using neural architectures to aggregate sequence labels from multiple annnotators
- New article on improving biomedical extractive summarisation using domain knowledge